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One of CS230's main goals is to prepare you to apply machine learning algorithms to real-world tasks, or to leave you well-qualified to start machine learning or AI research. The final project is intended to start you in these directions.
Time and Location
Getting started, project starter package.
The teaching team has put together a
- github repository with project code examples, including a computer vision and a natural language processing example (both in Tensorflow and Pytorch).
- A series of posts to help you familiarize yourself with the project code examples, get ideas on how to structure your deep learning project code, and to setup AWS. The code examples posted are optional and are only meant to help you with your final project. The code can be reused in your projects, but the examples presented are not complex enough to meet the expectations of a quarterly project.
- A sheet of resources to get started with project ideas in several topics
This quarter in CS230, you will learn about a wide range of deep learning applications. Part of the learning will be online, during in-class lectures and when completing assignments, but you will really experience hands-on work in your final project. We would like you to choose wisely a project that fits your interests. One that would be both motivating and technically challenging.
Most students do one of three kinds of projects:
- Application project. This is by far the most common: Pick an application that interests you, and explore how best to apply learning algorithms to solve it.
- Algorithmic project. Pick a problem or family of problems, and develop a new learning algorithm, or a novel variant of an existing algorithm, to solve it.
- Theoretical project. Prove some interesting/non-trivial properties of a new or an existing learning algorithm. (This is often quite difficult, and so very few, if any, projects will be purely theoretical.) Some projects will also combine elements of applications and algorithms.
Many fantastic class projects come from students picking either an application area that they’re interested in, or picking some subfield of machine learning that they want to explore more. So, pick something that you can get excited and passionate about! Be brave rather than timid, and do feel free to propose ambitious things that you’re excited about. (Just be sure to ask us for help if you’re uncertain how to best get started.) Alternatively, if you’re already working on a research or industry project that deep learning might apply to, then you may already have a great project idea.
A very good CS230 project will be a publishable or nearly-publishable piece of work. Each year, some number of students continue working on their projects after completing CS230, submitting their work to a conferences or journals. Thus, for inspiration, you might also look at some recent deep learning research papers. Two of the main machine learning conferences are ICML and NeurIPS . You may also want to look at class projects from previous years of CS230 ( Fall 2017 , Winter 2018 , Spring 2018 , Fall 2018 ) and other machine learning/deep learning classes ( CS229 , CS229A , CS221 , CS224N , CS231N ) is a good way to get ideas. Finally, we crowdsourced and curated a list of ideas that you can view here , and an older one here , and a (requires Stanford login).
Once you have identified a topic of interest, it can be useful to look up existing research on relevant topics by searching related keywords on an academic search engine such as: http://scholar.google.com . Another important aspect of designing your project is to identify one or several datasets suitable for your topic of interest. If that data needs considerable pre-processing to suit your task, or that you intend to collect the needed data yourself, keep in mind that this is only one part of the expected project work, but can often take considerable time. We still expect a solid methodology and discussion of results, so pace your project accordingly.
Notes on a few specific types of projects:
- Computation power. Amazon Web Services is sponsoring the CS230 projects by providing you with GPU credits to run your experiments! We will update regarding how to retrieve your GPU credits. Alternatively Google Cloud and Microsoft Azure offer free academic units which you can apply to.
- Preprocessed datasets. While we don’t want you to have to spend much time collecting raw data, the process of inspecting and visualizing the data, trying out different types of preprocessing, and doing error analysis is often an important part of machine learning. Hence if you choose to use preprepared datasets (e.g. from Kaggle, the UCI machine learning repository, etc.) we encourage you to do some data exploration and analysis to get familiar with the problem.
- Replicating results. Replicating the results in a paper can be a good way to learn. However, we ask that instead of just replicating a paper, also try using the technique on another application, or do some analysis of how each component of the model contributes to final performance.
This section contains the detailed instructions for the different parts of your project.
Groups: The project is done in groups of 1-3 people; teams are formed by students.
Submission: We will be using Gradescope for submission of all four parts of the final project. We’ll announce when submissions are open for each part. You should submit on Gradescope as a group: that is, for each part, please make one submission for your entire project group and tag your team members.
Evaluation: We will not be disclosing the breakdown of the 40% that the final project is worth amongst the different parts, but the video and final report will combine to be the majority of the grade. Attendance and participation during your TA meetings will also be considered. Projects will be evaluated based on:
- The technical quality of the work. (I.e., Does the technical material make sense? Are the things tried reasonable? Are the proposed algorithms or applications clever and interesting? Do the authors convey novel insight about the problem and/or algorithms?)
- Significance. (Did the authors choose an interesting or a “real” problem to work on, or only a small “toy” problem? Is this work likely to be useful and/or have impact?)
- The novelty of the work. (Is this project applying a common technique to a well-studied problem, or is the problem or method relatively unexplored?)
In order to highlight these components, it is important you present a solid discussion regarding the learnings from the development of your method, and summarizing how your work compares to existing approaches.
Deadline: October 12, Wednesday 11:59 PM
First, make sure to submit the following Google form so that we can match you to a TA mentor. In the form you will have to provide your project title, team members and relevant research area(s).
In the project proposal, you’ll pick a project idea to work on early and receive feedback from the TAs. If your proposed project will be done jointly with a different class’ project, you should obtain approval from the other instructor and approval from us. Please come to the project office hours to discuss with us if you would like to do a joint project. You should submit your proposals on Gradescope. All students should already be added to the course page on Gradescope via your SUNet IDs. If you are not, please create a private post on Ed and we will give you access to Gradescope.
In the proposal, below your project title, include the project category. The category can be one of:
- Computer Vision
- Natural Language Processing
- Generative Modeling
- Speech Recognition
- Reinforcement Learning
- Others (Please specify!)
Your project proposal should include the following information:
- What is the problem that you will be investigating? Why is it interesting?
- What are the challenges of this project?
- What dataset are you using? How do you plan to collect it?
- What method or algorithm are you proposing? If there are existing implementations, will you use them and how? How do you plan to improve or modify such implementations?
- What reading will you examine to provide context and background? If relevant, what papers do you refer to?
- How will you evaluate your results? Qualitatively, what kind of results do you expect (e.g. plots or figures)? Quantitatively, what kind of analysis will you use to evaluate and/or compare your results (e.g. what performance metrics or statistical tests)?
Presenting pointers to one relevant dataset and one example of prior research on the topic are a valuable (optional) addition. We link one past example of a good project proposal here and a latex template .
Deadline: November 11, Friday 11:59 PM
The milestone will help you make sure you’re on track, and should describe what you’ve accomplished so far, and very briefly say what else you plan to do. You should write it as if it’s an “early draft” of what will turn into your final project. You can write it as if you’re writing the first few pages of your final project report, so that you can re-use most of the milestone text in your final report. Please write the milestone (and final report) keeping in mind that the intended audience is Profs. Ng and Katanforoosh and the TAs. Thus, for example, you should not spend two pages explaining what logistic regression is. Your milestone should include the full names of all your team members and state the full title of your project. Note: We will expect your final writeup to be on the same topic as your milestone. In order to help you the most, we expect you to submit your running code. Your code should contain a baseline model for your application. Along with your baseline model, you are welcome to submit additional parts of your code such as data pre-processing, data augmentation, accuracy matric(s), and/or other models you have tried. Please clean your code before submitting, comment on it, and cite any resources you used. Please do not submit your dataset . However, you may include a few samples of your data in the report if you wish.
Submission Deadline: December 9, Friday 11:59 PM (No late days allowed)
Your video is required to be a 3-4 minute summary of your work. There is a hard limit of 4 minutes, and TAs will not watch a video beyond the 4 minute mark. Include diagrams, figures and charts to illustrate the highlights of your work. The video needs to be visually appealing, but also illustrate technical details of your project.
If possible, try to come up with creative visualizations of your project. These could include:
- System diagrams
- More detailed examples of data that don’t fit in the space of your report
- Live demonstrations for end-to-end systems
We recommend searching for conference presentation sessions (AAAI, Neurips, ECCV, ICML, ICLR etc) and following those formats.
You can find a sample video from a previous iteration of the class here
Deadline: December 9, Friday 11:59 PM (No late days allowed)
The final report should contain a comprehensive account of your project. We expect the report to be thorough, yet concise. Broadly, we will be looking for the following:
- Good motivation for the project and an explanation of the problem statement
- A description of the data
- Any hyperparameter and architecture choices that were explored
- Presentation of results
- Analysis of results
- Any insights and discussions relevant to the project
After the class, we will post all the final writeups online so that you can read about each other’s work. If you do not want your write-up to be posted online, then please create a private Piazza post.
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Top 16 Exciting Deep Learning Project Ideas for Beginners 
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Deep Learning Project Ideas
Although a new technological advancement, the scope of Deep Learning is expanding exponentially. This technology aims to imitate the biological neural network, that is, of the human brain. While the origins of Deep Learning dates back to the 1950s, it is only with the advancement and adoption of Artificial Intelligence and Machine Learning that it came to the limelight. So, if you are an ML beginner, the best thing you can do is work on some Deep learning project ideas.
You don’t have to waste time finding the best deep learning research topic for you. This article includes a variety of deep learning project topics in a categorised manner
We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting deep learning project ideas which beginners can work on to put their knowledge to test. In this article, you will find top deep learning project ideas for beginners to get hands-on experience on deep learning.
A subset of Machine Learning, Deep Learning leverages artificial neural networks arranged hierarchically to perform specific ML tasks. Deep Learning networks use the unsupervised learning approach – they learn from unstructured or unlabeled data. Artificial neural networks are just like the human brain, with neuron nodes interconnected to form a web-like structure.
While traditional learning models analyze data using a linear approach, the hierarchical function of Deep Learning systems is designed to process and analyze data in a nonlinear approach.
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Deep Learning architectures like deep neural networks, recurrent neural networks, and deep belief networks have found applications in various fields including natural language processing, computer vision, bioinformatics, speech recognition, audio recognition, machine translation, social network filtering, drug design, and even board game programs. As new advances are being made in this domain, it is helping ML and Deep Learning experts to design innovative and functional Deep Learning projects. The more deep learning project ideas you try, the more experience you gain.
Today, we’ll discuss the top seven amazing Deep Learning projects that are helping us reach new heights of achievement.
In this article, we have covered top deep learning project ideas . We started with some beginner projects which you can solve with ease. Once you finish with these simple projects, I suggest you go back, learn a few more concepts and then try the intermediate projects. When you feel confident, you can then tackle the advanced projects. If you wish to improve your skills on the same, you need to get your hands on these machine learning courses .
So, here are a few Deep Learning Project ideas which beginners can work on:
Deep Learning Project Ideas: Beginners Level
This list of deep learning project ideas for students is suited for beginners, and those just starting out with ML in general. These deep learning project ideas will get you going with all the practicalities you need to succeed in your career.
Further, if you’re looking for deep learning project ideas for final year , this list should get you going. So, without further ado, let’s jump straight into some deep learning project ideas that will strengthen your base and allow you to climb up the ladder.
1. Image Classification with CIFAR-10 dataset
One of the best ideas to start experimenting you hands-on deep learning projects for students is working on Image classification. CIFAR-10 is a large dataset containing over 60,000 (32×32 size) colour images categorized into ten classes, wherein each class has 6,000 images. The training set contains 50,000 images, whereas the test set contains 10,000 images. The training set will be divided into five separate sections, each having 10,000 images arranged randomly. As for the test set, it will include 1000 images that are randomly chosen from each of the ten classes.
In this project, you’ll develop an image classification system that can identify the class of an input image. Image classification is a pivotal application in the field of deep learning, and hence, you will gain knowledge on various deep learning concepts while working on this project.
2. Visual tracking system
A visual tracking system is designed to track and locate moving object(s) in a given time frame via a camera. It is a handy tool that has numerous applications such as security and surveillance, medical imaging, augmented reality, traffic control, video editing and communication, and human-computer interaction.
This system uses a deep learning algorithm to analyze sequential video frames, after which it tracks the movement of target objects between the frames. The two core components of this visual tracking system are:
- Target representation and localization
- Filtering and data association
3. Face detection system
This is one of the excellent deep learning project ideas for beginners. With the advance of deep learning, facial recognition technology has also advanced tremendously. Face recognition technology is a subset of Object Detection that focuses on observing the instance of semantic objects. It is designed to track and visualize human faces within digital images.
In this deep learning project, you will learn how to perform human face recognition in real-time. You have to develop the model in Python and OpenCV.
Deep Learning Project Ideas: Intermediate Level
4. digit recognition system.
As the name suggests, this project involves developing a digit recognition system that can classify digits based on the set tenets. Here, you’ll be using the MNIST dataset containing images (28 X 28 size).
This project aims to create a recognition system that can classify digits ranging from 0 to 9 using a combination of shallow network and deep neural network and by implementing logistic regression. Softmax Regression or Multinomial Logistic Regression is the ideal choice for this project. Since this technique is a generalization of logistic regression, it is apt for multi-class classification, assuming that all the classes are mutually exclusive).
In this project, you will model a chatbot using IBM Watson’s API. Watson is the prime example of what AI can help us accomplish. The idea behind this project is to harness Watson’s deep learning abilities to create a chatbot that can engage with humans just like another human being. Chatbots are supremely intelligent and can answer to human question or requests in real-time. This is the reason why an increasing number of companies across all domains are adopting chatbots in their customer support infrastructure.
This project isn’t a very challenging one. All you need is to have Python 2/3 in your machine, a Bluemix account, and of course, an active Internet connection! If you wish to scale it up a notch, you can visit Github repository and improve your chatbot’s features by including an animated car dashboard.
Read: How to make chatbot in Python?
6. Music genre classification system
This is one of the interesting deep learning project ideas. This is an excellent project to nurture and improve your deep learning skills. You will create a deep learning model that uses neural networks to classify the genre of music automatically. For this project, you will use an FMA ( Free Music Archive ) dataset. FMA is an interactive library comprising high-quality and legal audio downloads. It is an open-source and easily accessible dataset that is great for a host of MIR tasks, including browsing and organizing vast music collections.
However, keep in mind that before you can use the model to classify audio files by genre, you will have to extract the relevant information from the audio samples (like spectrograms, MFCC, etc.).
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7. drowsiness detection system.
The drowsiness of drivers is one of the main reasons behind road accidents. It is natural for drivers who frequent long routes to doze off when behind the steering wheel. Even stress and lack of sleep can cause drivers to feel drowsy while driving. This project aims to prevent and reduce such accidents by creating a drowsiness detection agent.
Here, you will use Python, OpenCV, and Keras to build a system that can detect the closed eyes of drivers and alert them if ever they fall asleep while driving. Even if the driver’s eyes are closed for a few seconds, this system will immediately inform the driver, thereby preventing terrible road accidents. OpenCV will monitor and collect the driver’s images via a webcam and feed them into the deep learning model that will classify the driver’s eyes as ‘open’ or ‘closed.’
8. Image caption generator
This is one of the trending deep learning project ideas. This is a Python-based deep learning project that leverages Convolutional Neural Networks and LTSM (a type of Recurrent Neural Network) to build a deep learning model that can generate captions for an image.
An Image caption generator combines both computer vision and natural language processing techniques to analyze and identify the context of an image and describe them accordingly in natural human languages (for example, English, Spanish, Danish, etc.). This project will strengthen your knowledge of CNN and LSTM, and you will learn how to implement them in real-world applications as this.
9. Colouring old B&W photos
For long, automated image colourization of B&W images has been a hot topic of exploration in the field of computer vision and deep learning. A recent study stated that if we train a neural network using a voluminous and rich dataset, we could create a deep learning model that can hallucinate colours within a black and white photograph.
In this image colourization project, you will be using Python and OpenCV DNN architecture (it is trained on ImageNet dataset). The aim is to create a coloured reproduction of grayscale images. For this purpose, you will use a pre-trained Caffe model , a prototxt file, and a NumPy file.
Deep Learning Project Ideas – Advanced Level
Below are some best ideas fo r advanced deep learning projects . These are some deep learning research topic s that will definitely challenge your depth of knowledge.
Detectron is a Facebook AI Research’s (FAIR) software system designed to execute and run state-of-the-art Object Detection algorithms. Written in Python, this Deep Learning project is based on the Caffe2 deep learning framework.
Detectron has been the foundation for many wonderful research projects including Feature Pyramid Networks for Object Detection ; Mask R-CNN ; Detecting and Recognizing Human-Object Interactions ; Focal Loss for Dense Object Detection ; Non-local Neural Networks , and Learning to Segment Every Thing , to name a few.
Detectron offers a high-quality and high-performance codebase for object detection research. It includes over 50 pre-trained models and is extremely flexible – it supports rapid implementation and evaluation of novel research.
This is one of the interesting deep learning project ideas. WaveGlow is a flow-based Generative Network for Speech Synthesis developed and offered by NVIDIA. It can generate high-quality speech from mel-spectograms. It blends the insights obtained from WaveNet and Glow to facilitate fast, efficient, and high-quality audio synthesis, without requiring auto-regression.
WaveGlow can be implemented via a single network and also trained using a single cost function. The aim is to optimize the likelihood of the training data, thereby makes the training procedure manageable and stable.
OpenCog project includes the core components and a platform to facilitate AI R&D. It aims to design an open-source Artificial General Intelligence (AGI) framework that can accurately capture the spirit of the human brain’s architecture and dynamics. The AI bot, Sophia is one of the finest examples of AGI.
OpenCog also encompasses OpenCog Prime – an advanced architecture for robot and virtual embodied cognition that includes an assortment of interacting components to give birth to human-equivalent artificial general intelligence (AGI) as an emergent phenomenon of the system as a whole.
DeepMimic is an “example-guided Deep Reinforcement Learning of Physics-based character skills.” In other words, it is a neural network trained by leveraging reinforcement learning to reproduce motion-captured movements via a simulated humanoid, or any other physical agent.
The functioning of DeepMimic is pretty simple. First, you need to set up a simulation of the thing you wish to animate (you can capture someone making specific movements and try to imitate that). Now, you use the motion capture data to train a neural network through reinforcement learning. The input here is the configuration of the arms and legs at different time points while the reward is the difference between the real thing and the simulation at specific time points.
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14. ibm watson.
One of the most excellent examples of Machine Learning and Deep Learning is IBM Watson. The greatest aspect of IBM Watson is that it allows Data Scientists and ML Engineers/Developers to collaborate on an integrated platform to enhance and automate the AI life cycle. Watson can simplify, accelerate, and manage AI deployments, thereby enabling companies to harness the potential of both ML and Deep Learning to boost business value.
IBM Watson is Integrated with the Watson Studio to empower cross-functional teams to deploy, monitor, and optimize ML/Deep Learning models quickly and efficiently. It can automatically generate APIs to help your developers incorporate AI into their applications readily. On top of that, it comes with intuitive dashboards that make it convenient for the teams to manage models in production seamlessly.
15. Google Brain
This is one of the excellent deep learning project ideas. The Google Brain project is Deep Learning AI research that began in 2011 at Google. The Google Brain team led by Google Fellow Jeff Dean, Google Researcher Greg Corrado, and Stanford University Professor Andrew Ng aimed to bring Deep Learning and Machine Learning out from the confines of the lab into the real world. They designed one of the largest neural networks for ML – it comprised of 16,000 computer processors connected together.
To test the capabilities of a neural network of this massive size, the Google Brain team fed the network with random thumbnails of cat images sourced from 10 million YouTube videos. However, the catch is that they didn’t train the system to recognize what a cat looks like. But the intelligent system left everyone astonished – it taught itself how to identify cats and further went on to assemble the features of a cat to complete the image of a cat!
The Google Brain project successfully proved that software-based neural networks can imitate the functioning of the human brain, wherein each neuron is trained to detect particular objects. How Deep Learning Algorithms are Transforming our Everyday Lives
16. 12 Sigma’s Lung Cancer detection algorithm
12 Sigma has developed an AI algorithm that can reduce diagnostic errors associated with lung cancer in its early stages and detect signs of lung cancer much faster than traditional approaches.
According to Xin Zhong, the Co-founder and CEO of Sigma Technologies, usually conventional cancer detection practices take time to detect lung cancer. However, 12 Sigma’s AI algorithm system can reduce the diagnosis time, leading to a better rate of survival for lung cancer patients.
Generally, doctors diagnose lung cancer by carefully examining CT scan images to check for small nodules and classify them as benign or malignant. It can take over ten minutes for doctors to visually inspect the patient’s CT images for nodules, plus additional time for classifying the nodules as benign or malignant.
Needless to say, there always remains a high possibility of human errors. 12 Sigma maintains that its AI algorithm can inspect the CT images and classify nodules within two minutes .
One of the most famous types of artificial neural networks is CNN, also known as Convolutional Neural Networks which is majorly used for image and object recognition as well as classification. It is a type of supervised Deep Learning, which means that is it able to learn on its own, without any human supervision. It can work with both structured and unstructured data. CNN.
By integrating CNNs and deep learning world-class applications are made. Some of the applications of CNN include Facial recognition, analyzing documents, collecting natural history, analyzing climate and even advertisements. Especially in the world of marketing and advertisements, CNN has brought a huge change by introducing data-driven personalized advertising.
Therefore, it is fair to say that CNN deep learning projects can help add significant weight to one’s experience and resume. Below are some ideas for CNN deep learning projects. These projects are best for beginners to advanced levels to get a hands-on experience with CNN. As you go down the list, difficulty levels increases.
- Disease detection in plants using MATLAB
With the constantly shifting climate changes and various other pathogenic bacteria and fungus, the life span of the plants are getting decreased. Especially due to the use of harsh pesticides, a new type of disease may emerge within a plant. Diagnosing these problems at an early stage can help us save a variety of plant species that are on the verge of extinction and these deep learning research topic s assist to make that happen.
It is pretty time-consuming to manually detect the disease, therefore image processing can help make the process swifter. In this project, machine vision equipment is used to collect images and judge whether or not the plant has any fatal disease. It is quite a popular topic among CNN deep learning projects. Manual design of features plus classifier or conventional image processing algorithm is used in this project.
Knowledge of MATLAB is essential to execute this project. Apart from this knowledge of Image processing and CNNs is also required.
- Detecting traffic using Python
This recognition system helps figure out the traffic signal lights along the stress, speed limit signs and various other signs such as caution signs, blend signs and so on. This recognition system is exp[ected to play a huge role in smart vehicles and self-driving cars in the future.
As per the reports of WHO, on average approx, 1.3 million people die every year due to road traffic crashes and about 20 to 50 million people suffer from fatal and non-fatal road accidents. Therefore, a system like this can play a significant role in reducing the numbers.
This program requires the knowledge of Python, CNN and Build CNN. It also requires some basic knowledge of Keras, some Python library Matplotlip, PIL, image classification and Scikit-learn.
- Detecting Gender and Age:
As simple as it may sound, after the emergence of AI, it has become so important to differentiate between real and mimic. Detecting age and gender is a project that has been around for quite a long now. However, after the emergence of AI, the process has become a bit tricky. Continuous changes are been made to improve the outcomes.
The programing language that is used for executing this project in Python. The objective of this program is to give an approximate idea of the person’s gender and age by using their pictures. To execute this project, you’ll be required to have an in-depth knowledge of Python, OpenCV and CNNs.
- Language Translating
With the speed at which globalization is becoming the new norm, knowing multiple languages is becoming more and more important. However, not everyone has the knack or interest to learn multiple languages. Apart from that, oftentimes we are required to know a certain language even for travelling purposes. The majority of us rely on Google Translator which functions on the basics of Machine Translation (MT).
It is an in-demand topic under computer linguistics where ML is used to translate one language to another. However, it is under more advanced deep learning projects . Amongst the varied choices, Neural Machine Translation (NMT) is considered to be the most efficient method. Having knowledge of RNN sequence-to-sequence learning is important for this project.
- Hand gesture recognition
Smart devices such as TVs, mobile phones and cameras are becoming more advanced every day. We all are familiar with the feature of gesture control in our smartphones, however, it can also be implemented in devices such as TVs.
By incorporating gesture recognition programs into TVs, people will be able to perform a bunch of basic tasks without having the need of using remotes. Tasks like changing channels, increasing volume, pausing, and fast-forwarding, all can be done with the help of gesture recognition.
For this few hundred of training, data are required, which can then be classified into the major classes, like the ones mentioned before. These videos of various people performing the hand gestures will be used as training data, and when anybody does a similar hand gesture, it will be detected by the smart TV’s webcam and behave accordingly. It is definitely a deep learning project that is more on the advanced side.
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These are only a handful of the real-world applications of Deep Learning made so far. The technology is still very young – it is developing as we speak. Deep Learning holds immense possibilities to give birth to pioneering innovations that can help humankind to address some of the fundamental challenges of the real world .
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Is Deep Learning just a hype or does it have real-life applications?
Deep Learning has recently found a number of useful applications. Deep learning is already changing a number of organizations and is projected to bring about a revolution in practically all industries, from Netflix's well-known movie recommendation system to Google's self-driving automobiles. Deep learning models are utilized in everything from cancer diagnosis to presidential election victory, from creating art and literature to making actual money. As a result, it would be incorrect to dismiss it as a fad. At any given time, Google and Facebook are translating content into hundreds of languages. This is accomplished by the application of deep learning models to NLP tasks, and it is a big success story.
What is the difference between Deep Learning and Machine Learning?
The most significant distinction between deep learning and regular machine learning is how well it performs when data scales up. Deep learning techniques do not perform well when the data is small. This is due to the fact that deep learning algorithms require a vast amount of data to fully comprehend it. Traditional machine learning algorithms, on the other hand, with their handmade rules, win in this circumstance. Most used features in machine learning must be chosen by an experienced and then hand-coded according to the domain and data type.
What are the prerequisites for starting out in Deep Learning?
Starting out with deep learning isn't nearly as difficult as some people make it out to be. Before getting into deep learning, you should brush up on a few fundamentals. Probability, derivatives, linear algebra, and a few other fundamental concepts should be familiar to you. Any machine learning task necessitates a fundamental understanding of statistics. Deep learning in real-world issues necessitates a reasonable level of coding ability. Deep learning is built on the foundation of machine learning. Without first grasping the basics of machine learning, it is impossible to begin mastering deep learning.
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Top 20 Deep Learning Projects With Source Code
What is deep learning, use of deep learning, deep learning projects for beginners, 1. image classification using cifar-10 dataset, 2. dog’s breed identification , 3. human face detection, 4. music genre classification system, intermediate deep learning projects, 5. drowsy driver detection system, 6. breast cancer detection ssing deep learning, 7. gender recognition using voice, 9. color detection system, 10. crop disease detection, advanced deep learning projects, 11. ocr (optical character reader) using yolo and tesseract for text extraction, 12. real-time image animation, 13. store item demand forecasting, 14. fake news detection project, 15. coloring old black and white photos, 16. human pose detection, 17. language translator using deep learning, 18. typing assistant, 19. hand gesture recognition system, 20. lane detection and assistance system, frequently asked questions, additional resources.
Despite being a relatively new scientific innovation, the scope of Deep Learning is rapidly expanding. The goal of this technology is to mimic the biological neural network of the human brain. Human brains have neurons that send and receive signals, forming the basis of Neural Networks. While Deep Learning has its roots in the 1950s, it was only recently brought to light by the growth and adoption of Artificial Intelligence and Machine Learning. If you’re new to machine learning, the best thing you can do is brainstorm Deep Learning project ideas. To assist you in your quest, we are going to suggest 20 Deep learning and Neural Network projects.
Deep learning refers to a class of machine learning techniques that employ numerous layers to extract higher-level features from raw data. Lower layers in image processing, for example, may recognize edges, whereas higher layers may identify human-relevant notions like numerals, letters, or faces. Deep learning uses artificial neural networks, which are supposed to mimic how humans think and learn, as opposed to machine learning, which uses simpler principles. Up until recently, the complexity of neural networks was constrained by processing capacity. Larger, more powerful neural networks are now possible thanks to advances in Big Data analytics, allowing computers to monitor, learn, and react to complicated events faster than people. Image categorization, language translation, and speech recognition have all benefited from deep learning. It can tackle any pattern recognition problem without the need for human intervention.
We could never have envisaged deep learning applications bringing us self-driving cars and virtual assistants like Alexa, Siri, and Google Assistant just a few years ago. However, these innovations are already a part of our daily lives. Deep Learning continues to fascinate us with its almost limitless applications, including fraud detection and pixel restoration. Apart from these, Deep learning finds its application in the following industries:
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- Virtual assistants
- Customer experience
- Computer vision
- Language translation
In a real-time work environment, theoretical knowledge alone will not be sufficient. In this article, we’ll look at some fun deep learning project ideas that beginners, as well as experienced, can use to put their skills to the test. The projects covered in this article will serve those who want to get some hands-on experience with the technology. 20 projects along with their GitHub source code link are provided below.
In this project, you’ll create an image classification system that can determine the image’s class. Because image classification is such an important application in the field of deep learning, working on this project will allow you to learn about a variety of deep learning topics.
Working on image categorization is one of the finest ways to get started with hands-on deep learning projects for students. CIFAR-10 is a big dataset including approximately 60,000 color images (3232 sizes) divided into ten classes, each with 6,000 images. There are 50,000 photos in the training set and 10,000 images in the test set. The training set will be divided into five portions, each containing 10,000 photos that will be organized in random order. The test set will consist of 1000 photos selected at random from each of the ten classes.
Link to the source code
How frequently do you find yourself wondering about a dog’s breed name? There are numerous dog breeds, and most of them are very similar. Using the dog breeds dataset, we can create a model that can categorize different dog breeds based on an image. Dog lovers will benefit from this endeavor.
To implement this, a convolutional neural network is an obvious solution to an image recognition challenge. Unfortunately, due to the limited number of training examples, any CNN trained just on the provided training images would be highly overfitting. To overcome this, the developer used Resnet18’s transfer learning to give my model a head start and dramatically reduce training challenges. The model was able to be complex enough to accurately identify the dogs thanks to the deep structure.
Face detection is a computer vision problem that entails identifying people in photographs. It’s a simple difficulty for people to solve, and classical feature-based algorithms like the cascade classifier have done a good job at it. On typical benchmark face identification datasets, deep learning algorithms have recently attained state-of-the-art results. We can create models that detect the bounding boxes of the human face with excellent accuracy. This project will teach you how to detect any object in an image in general, and get you started with object detection.
This is an impressive deep learning project concept. You’ll build a deep learning model that employs neural networks to automatically classify music genres. The model takes as an input the spectogram of music frames and analyzes the image using a Convolutional Neural Network (CNN) plus a Recurrent Neural Network (RNN). The system’s output is a vector of the song’s projected genres. The model has been refined with a tiny sample (30 songs per genre) before testing it on the GTZAN dataset, resulting in an accuracy of 80%.
One of the leading causes of traffic accidents is driver drowsiness. It’s natural for drivers who travel long distances to fall asleep behind the wheel. Drivers might become tired while driving due to a variety of factors, including stress and lack of sleep. By developing a drowsy detection agent, our study hopes to avoid and reduce such accidents. You’ll use Python, OpenCV, and Keras to create a system that can detect drivers’ closed eyes and alarm them if they fall asleep behind the wheel. Even if the driver’s eyes are closed for a few seconds, this technology will alert the driver, preventing potentially fatal road accidents. We will use OpenCV to collect photos from a camera and feed them into a Deep Learning model that will classify whether the person’s eyes are ‘Open’ or ‘Closed’ in this project. For this project, we’ll take the following approach:
Step 1- Take an image from a camera as input.
Step 2 -Create a Region of Interest around the face in the image (ROI).
Step 3- Use the ROI to find the eyes and input them to the classifier.
Step 4- The classifier will determine whether the eyes are open.
Step 5- Calculate the score to see if the person is sleepy.
Cancer is a severe disease that needs to be caught as soon as possible. Histopathology photos can be used to diagnose malignancy. Cancer cells differ from normal cells, therefore, we can use an image classification algorithm to identify the disease at the earliest. Deep Learning models have achieved a high level of accuracy in this field. The accuracy of the model depends upon the training data set provided to it.
Breast cancer is the most frequent cancer in women, and the most common type of breast cancer is invasive ductal carcinoma (IDC). Automated approaches can be utilized to save time and reduce errors for detecting and categorizing breast cancer subtypes, which is a crucial clinical activity.
We can accurately determine a person’s gender by listening to their voice. Machines can also be taught to distinguish between male and female voices. We’ll need audio clips with male and female gender labels. The data is then fed into the classifying model using feature extraction techniques. The link to the source code of the project has been provided below. This project can be extended further to identify the mood of the speaker.
Making a chatbot using deep learning algorithms is another fantastic endeavor. Chatbots can be implemented in a variety of ways, and a smart chatbot will employ deep learning to recognize the context of the user’s question and then offer the appropriate response.
The project given below is a beginner’s walk-through tutorial on how to build a chatbot with deep learning, TensorFlow, and an NMT sequence-to-sequence model.
The project given below can predict up to 11 Distinct Color Classes based on the RGB input by users from the sliders. Red, Green, Blue, Yellow, Orange, Pink, Purple, Brown, Grey, Black, and White are the 11 classes. R: Red, G: Green, B: Blue; Each of which is basically an integer ranging from 0 to 255; and these combined Red, Green, and Blue values are utilized to form a distinct Solid Color for every pixel on the computer, mobile, or any electronic screen. This Classifier predicts the solid color’s color class. Also, the color dataset has been humanly developed to make the artificial model(classifier) classify the colors as humanly as possible.
When it comes to using technology in agriculture, one of the most perplexing issues is plant disease detection. Despite the fact that research has been done to determine whether a plant is healthy or diseased utilizing Deep Learning and Neural Networks, new technologies are continually being developed.
You must create a model that uses RGB photos to forecast illnesses in crops for this assignment. Convolutional Neural Networks (CNN) are utilized to create a crop disease detection model. CNN uses an image to identify and detect sickness. In a Convolutional Neural Network, there are several steps. These are the steps:
- Operation of Convolution.
- Layer of ReLU
- Full connection
Extracting information from any document is a difficult operation that requires object classification and object localization. In many financial, accounting, and taxation fields, OCR digitization addresses the difficulty of automatically extracting, which plays a significant role in speeding document-intensive operations and office automation.
This custom OCR combines YOLO and Tesseract to read the contents of a Lab Report and convert it to an editable format. In this case, the developer of the project has utilized YOLO V3 that was trained on a personal dataset. The coordinates of the discovered objects are then supplied to cropping and storing the detected objects in another list. To get the required output, this list is fed into the Tesseract.
This is an open-source computer vision project. You must use OpenCV to accomplish real-time image animation in this project. The model modifies the image expression to match the expression of the person in front of the camera.
Using this repository, you will be able to make face image animations using a real-time camera image of your face, from a webcam animation or, if you already have a video of your face, you may use that to make face image animations. This assignment is particularly valuable if you aim to work in the fashion, retail, or advertising industries. This project’s code is available on GitHub.
Building a forecasting model to estimate store item demand is difficult due to numerous external factors such as the store’s location, seasonality, changes in the store’s neighborhood or competitive position, a considerable variance in the number of consumers and goods, and so on. With such a large volume of data, no human planner could possibly examine all of the possible elements. Deep learning, on the other hand, makes it easier by taking these characteristics into account at a finer level, by individual store or fulfillment channel.
Consumers can now get the most up-to-date news at their fingertips thanks to the digital age of mobile applications. But, are the things we read on these sites always accurate? No, that is not the case. Take, for example, our favorite chat application WhatsApp in real-time. You would have gotten a lot of notifications about how to cure and prevent the COVID-19 virus. These messages are frequently fraudulent, and the terrible aspect is that many people believe them and even follow them, which has led to some dangerous outcomes. AI is being used by companies such as Facebook, Google, and others to detect and remove false news from their platforms.
There are a variety of approaches for attaining this goal, but the goal of this effort is to identify the fishy ones solely by glancing at the text. There are no graphs, social network analysis, or photos. Three deep learning architectures are presented in this paper and then tested on two datasets (the fake news corpus and the TI-CNN), yielding state-of-the-art results.
- LSTM (Long Short Term Memory) Based architecture
- CNN (Convolutional Neural Network) Based architecture
- BERT (Bidirectional Encoder Representations from transformers) Based architecture
Automated picture colorization of black-and-white photos has become a prominent topic in computer vision and deep learning research. Image colorization takes a grayscale (black and white) image as an input and outputs a colorized version of an old movie image. The output colorized film’s image should represent and match the semantic colors and tones of the input.
The network is built in four parts and gradually becomes more complex.
- The alpha network deals with how an image is transformed into RGB pixel values and later translated into LAB pixel values, changing the color space. It also builds a core intuition for how the network learns.
- The network in the beta version is very similar to the alpha version. The difference is that we use more than one image to train the network.
- The full version adds information from a pre-trained classifier. You can think of the information as 20% nature, 30% humans, 30% sky, and 20% brick buildings. It then learns to combine that information with the black and white photo.
- The GAN version uses Generative Adversarial Networks to make the coloring more consistent and vibrant.
Humans are expressive beings. This project was developed using deep learning concepts and it can detect the pose you make in front of the camera. Several methods for predicting Human Pose Estimation have been proposed. These algorithms frequently start by identifying the component parts, then understand the connections between them to estimate the pose. Activity Recognition, Motion Capture and Augmented Reality, Training Robots, and Motion Tracking for Consoles in the game industry are just a few of the real-world applications of knowing a person’s orientation.
Have you ever traveled to a new location and struggled to communicate in the native tongue? I’m sure you’ve tried to imitate the local language and accent with Google Translator at least once. Machine Translation (MT) is a popular topic of computer linguistics that focuses on translation from one language to another. NMT (Neural Machine Translation) has become the most effective method for performing this task as deep learning has grown in popularity and efficiency. We’ve all used Google Translator, which is the industry’s premier machine translation example. An NMT model’s main goal is to take a text input in any language and translate it into a different language as an output.
The developer of the current project has used RNN sequence-to-sequence learning in Keras to translate the English language to the French language.
Devices these days are capable of finishing our sentences even before we type them. Google began automatically finishing my sentence as soon as I started entering the title “Auto text completion and creation with De…” It correctly predicted Deep Learning in this scenario!
The project given below provides the ability to autocomplete words and predicts what the next word will be. This allows you to type faster, more intelligently, and with less effort.
The methodology used to implement the project is as follows:
- Counting words in Corpora: Counting of things in NLP is based on a corpus. NLTK (Natural Language Toolkit) provides a diverse set of corpora.
- N-Gram model: Probabilistic models are used to compute the likelihood of a complete sentence or to make a probabilistic prediction of the next word in a sequence. In this model, the conditional probability of a word is calculated based on the preceding words.
- Bigram model: In this model, we approximate the probability of a word given all the previous words by the conditional probability of the preceding word.
- Trigram model: A trigram model looks just the same as a bigram model, except that we condition on the two-previous words.
- Minimum Edit Distance: The minimum edit distance between two strings is a measurement of how similar two strings are to one another.
Suppose you want to create a cool feature in a smart TV that recognizes five various gestures made by the user and allows them to operate the TV without using a remote.
The webcam positioned on the TV continuously monitors the movements. Each gesture is associated with a distinct command:
- Increase the volume, please.
- Reducing the volume is a no-no.
- 10 seconds ‘Jump’ backward with the left swipe
- ‘Jump’ forward 10 seconds with a right swipe
- Stop: Put the movie on hold.
The project given below achieves that by using training data that consists of a few hundred videos categorized into one of the five classes. Each video (typically 2-3 seconds long) is divided into a sequence of 30 frames(images). These videos have been recorded by various people performing one of the five gestures in front of a webcam – similar to what the smart TV will use.
Automatic driving technology has advanced rapidly in recent years. One of the major concerns in the manufacturing of self-driving cars is the detection of the lane line. The given project is the implementation of lanenet model for real-time lane detection using a deep neural network model. In this project, you will implement a Deep Neural Network for real-time lane detection using TensorFlow, based on an IEEE IV conference article. For a real-time lane detection task, this model includes an encoder-decoder stage, a binary semantic segmentation stage, and instance semantic segmentation using a discriminative loss function
We have collected 20 deep learning projects that you can develop to polish your skills and improve your portfolio. The technology is still in its infancy; it is continually evolving as we speak. Deep Learning has enormous potential for spawning ground-breaking ideas that can aid humanity in addressing some of the world’s most pressing issues.
How do I start a deep learning project? You can always start with small projects and then move on to tough ones once you are confident enough.
What is CNN deep learning? A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning system that can take an input image, assign relevance (learnable weights and biases) to various aspects/objects in the image, and distinguish between them.
What is Keras API? Keras is a Python-based deep learning API that runs on top of TensorFlow, a machine learning platform. It was created with the goal of allowing for quick experimentation.
What is Kaggle used for? Kaggle is a website where you may share ideas, get inspired, compete against other data scientists, acquire new information and coding methods, and explore real-world data science applications.
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21 Machine Learning Projects [Beginner to Advanced Guide]
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Machine Learning Projects
Tips to generate your own machine learning project ideas, ml project tips.
While theoretical machine learning knowledge is important, hiring managers value production engineering skills above all when looking to fill a machine learning role. To become job-ready, aspiring machine learning engineers must build applied skills through project-based learning. Machine learning projects can help reinforce different technical concepts and can be used to showcase a dynamic skill set as part of your professional portfolio.
No matter your skill level, you’ll be able to find machine learning project ideas that excite and challenge you. For inspiration, we’ve gathered examples of real-world ML projects geared towards beginner, intermediate, and advanced skill levels. Using these projects as templates, we’ll explore what a completed project should look like and discuss actionable tips for building your own impressive machine learning project.
First, we’ll examine basic machine learning projects geared toward learners who are proficient with R or Python (the most renowned language in the field of data science and machine learning) programming language and want to experiment with machine learning fundamentals. Next, we’ll review ML project ideas suited for those with intermediate and adv anced machine learning skills .
Machine Learning Project Ideas for Beginners
Titanic survival project.
After striking an iceberg, the so-called “unsinkable” RMS Titanic disappeared into the icy waters of the North Atlantic on April 15th, 1912. Over half of the ship’s 2224 passengers and crew members perished, and demographic data shows that some people aboard were more likely to survive than others.
This Kaggle project asks participants to build a model that predicts passenger survival based on passenger information like ticket class, gender, age, port of embarkation, and more. Kaggle offers a training data set that participants can use to build their own machine learning models, which can be constructed locally or on Kaggle Kernels (a no-setup, customizable Jupyter Notebooks environment with free GPUs).
Identifying Twits on Twitter Using Natural Language Processing
Try your hand at determining the probability that a given tweet originated from a particular user with sentiment analysis. This natural language processing technique can scan thousands of text documents for specific filters in a matter of seconds. This technique is how Twitter, for example, can scan and separate out tweets that contain racist or misogynistic content.
For inspiration, check out Eugene Aiken’s application of natural language processing to determine the probability that certain tweets were published by either Donald Trump or Hillary Clinton. To conduct a similar project, you’ll need to pick two users, scrape their tweets, run your twitter data through a natural language processor, classify it with a machine learning algorithm, and use the predict-proba method to determine probabilities. Learn more about the original project here and download the data set here .
Housing Prices Prediction
This Kaggle competition will help you practice creative feature engineering as well as regression techniques like random forest and gradient boosting. The goal of the project is to predict the final sales price of a home based on the Ames Housing Dataset.
The data includes 79 explanatory variables that describe vital attributes of homes in the city of Ames, Iowa. These data points range from zoning classification to lot size, remodel date, proximity to a railroad, and even masonry veneer type. The effect of each characteristic on house prices might surprise you!
Google Search Analysis With Python
Google users perform roughly 3.5 billion search engine queries per day. If you’re wondering what people are Googling, try using Google Trends to analyze a keyword of your choice. Google Trends offers an API called pytrends, which Aman Kharwal used to analyze the performance of the keyword, “machine learning.”
Aman used this tool to pinpoint 10 countries with the highest number of searches for “machine learning,” and also determined how the number of “machine learning” search queries changed over time. After conducting his analysis, Aman then used data visualizations to communicate his findings. Try building your own data visualization and consider what story your results might tell, and how that information could be important in a business context.
Chatbot Using Python
Chatbots are AI-powered applications that simulate human conversation, and are often implemented to field simple customer queries online. If you’re interested in natural language processing, try creating a Chatbot with Python’s NTLK library.
First, you’ll need to compile a list of queries and their correlating responses for the chatbot. Next, you’ll run the program and try out your queries with the chatbot. Once you’re satisfied with your baseline chatbot, you can use additional Python NLP packages or add more queries. To get started, take a look at Aman Khalwar’s guide to creating a chatbot with Python .
If you’re curious about computer vision, check out this Kaggle competition , which invites participants to build a digit recognizer using the classic MNIST dataset of handwritten numbers. The MNIST dataset—commonly referred to as the “Hello World” of machine learnings—comes equipped with pre-extracted features, which will streamline your data processing. Overall, this competition is an excellent introduction to simple neural networks , computer vision fundamentals, and classification methods like SVMs (Support Vector Machines) and K-nearest neighbors.
The competition also includes links to Python tutorials, as well as information about the details of the dataset (including previously applied algorithms and their levels of success).
Python Recommendation Engine
Building a recommendation engine sounds like a difficult task for a beginner, but your code can be as simple or as complex as you’d like. To create a basic content-based recommendation system, you’ll just need to maintain a log of items a user has seen and liked and calculate the top-N most similar products that user has not yet seen. A simple collaborative filtering recommendation engine can be powered by a user-user similarity matrix that recommends items that similar users like.
To learn more about building a Python recommendation engine, check out this Kaggle notebook , which explains how to implement collaborative filtering and content-based filtering in Python to generate personalized recommendations. The notebook explores these concepts using a rich, rare dataset about article sharing.
Intermediate Machine Learning Projects
Finding Frauds While Tracking Imbalanced Data
From banking via smartphones to AI-fueled stock price prediction, the financial sector embraces a cloud-based future. Thanks to a rising financial crime rate , the importance of AI-powered fraud detection is greater than ever. But because fraudulent financial interactions comprise only a small portion of the total number of financial transactions that occur daily, analysts must figure out how to reliably detect fraud with imbalanced data.
Fraud detection is a classification problem that deals with imbalanced data, meaning the issue to be predicted (fraud) is in the minority. As such, predictive models often struggle to generate real business value from imbalanced data, and sometimes results may be incorrect.
To address the issue, you can implement three different strategies:
- A combined approach
A combination approach can strike a balance between precision and recall, but you may choose to prioritize one over the other depending on the demands of your project and your desired business outcomes. You can learn more about conducting fraud detection with imbalanced data here .
Market Basket Analysis
With this Kaggle dataset, you can deploy an apriori algorithm to analyze and predict customer purchasing behaviors, otherwise known as Market Basket Analysis. Retailers often use this modelling technique used by retailers to determine associations between items based on rules of conditional probability.
As per the theory of Market Basket Analysis, if a customer buys a certain group of items, that customer is likely to buy related items as well. For example, purchasing baby formula often carries a correlation with buying diapers. This particular Kaggle dataset contains information about customers’ grocery purchases.
Text summarization condenses a piece of text while preserving its meaning. Increasingly, text summarization is being automated with Natural Language Processing. Extractive text summarization uses a scoring function to identify and lift key pieces of text from a document and assemble them into an edited version of the original. Abstractive text summarization, however, uses advanced natural language processing techniques to generate a new, shorter version that conveys the same information.
To create a text summarization system with machine learning, you’ll need familiarity with Pandas, Numpy, and NTLK. You’ll also need to use unsupervised learning algorithms like the Glove method (developed by Stanford) for word representation. Find a step-by-step guide to text summarization system building here .
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Black Friday Sales Prediction
Want to work on a regression model and expand your feature engineering skills? With this practice problem from Analytics Vidhya , you can use retail sales data to make predictions about Black Friday sales.
The dataset contains demographic information for customers (including age, gender, marital status, location, and more) as well as product details and total purchase amounts. A training data set and a testing data set are available.
From emails to social media posts, 80% of extant text data is unstructured. Text mining is a way to extract valuable insights from this type of raw data. The process of text mining transforms unstructured text data into a structured format, facilitating identification of key patterns and relationships within data sets.
To try your hand at text mining, experiment with these publicly available text data sets , which are geared towards multi-level classification, which is an important aspect of natural language processing. The Extreme Classification Repository that contains these data sets also provides resources that can be used to evaluate the performance of multi-label algorithms.
Million Song Analysis
Use this subset of the Million Song Dataset to predict the release year of a song from its audio features. The songs are primarily commercial Western tracks dating from 1922 to 2011, although the dataset does not include any audio—it consists of derived features only.
The core of the dataset is feature analysis and metadata related to each track. Song descriptions include values expressing danceability, loudness, duration of the track in seconds, and much more.
Movie Recommendation Engine
Netflix uses collaborative filtering as part of its complex recommendation system, and with the MovieLens Dataset , you can too! Collaborative filtering recommendation engines analyze user behavior/preferences and similarities between users to predict what users will like.
For example, if User A rated Spiderman, Batman Returns, and X-Men highly and User B gave high ratings to Batman Returns, X-Men, and Wonder Woman, a collaborative filtering algorithm would identify that both users enjoy superhero movies. Based on their shared behaviors, the system would recommend Wonder Woman to User A and Spiderman to User B.
The MovieLens 1M Data Set contains 1,000,209 ratings of roughly 3,9000 movies from 6,040 MovieLens users who joined MovieLens in 2000. The data set notes the genre of each film as well as the gender, occupation, age, and zip code of each user. You can learn more about building a movie recommendation engine with this data set here .
Advanced Machine Learning Projects
Catching crooks on the hook using geo-mapping and cloud computing.
Global Fishing Watch is a website launched by Google in partnership with environmental nonprofits to monitor commercial fishing activities worldwide, with the goal of reducing overfishing, illegal fishing, and marine habitat destruction.
Global Fishing Watch identifies and tracks illegal fishing activity by harvesting GPS data from ships and processing GPS data and other important information with neural networks. 60 million data points from 300,000+ vessels are harvested daily, and the website’s algorithm has learned to classify these ships by type (sail, cargo, or fishing), fishing gear (grawl, longline, purse seine), and fishing behaviors (where and when a vessel is active).
Global Fishing Watch shares vessel tracking information publicly, meaning anyone can download the website’s data and even track commercial fishing activity in real time. To warm up, see if you can use supervised classification to determine whether a vessel is fishing.
Download Global Fishing Watch datasets and find links to GitHub documentation and details here .
Uber Helpful Customer Support Using Deep Learning
To resolve customer issues with efficiency and ease, Uber developed a machine learning tool called COTA (Customer Obsession Ticket Assistant) to process customer support tickets using “human-in-the-loop” model architecture. Essentially, COTA uses machine learning and natural language processing techniques to rank tickets, identify ticket issues, and suggest solutions.
This project is great inspiration for anyone interested in applied machine learning and actual implementation. Uber also used A/B testing to evaluate two versions of their COTA model to assess impacts on ticket handling time, customer satisfaction, and revenue. Consider learning more about COTA if you’re interested in deep learning projects that combine clever technical architecture with human input.
Barbie With Brains Using Deep Learning Algorithms
Talking dolls that regurgitate pre-recorded phrases are nothing new—but what if dolls could actually listen and respond to kids? Enter Hello Barbie .
To create Hello Barbie, Mattel used natural language processing and advanced audio analytics that enabled the doll to interact logically in conversation. With the push of a button cleverly integrated into her outfit, Hello Barbie was able to record conversations and upload them to servers operated by ToyTalk, where the data was analyzed.
While some were excited that the doll could learn about users over time, Hello Barbie was met with public backlash around privacy concerns and eventually was discontinued. While this application of natural language processing proved contentious, those interested in complex deep learning architectures might find inspiration in the mechanics of the project.
Netflix Artwork Personalization Using Artificial Intelligence
Netflix marshalls sophisticated AI solutions to personalize title recommendations for users . But personalization at Netflix doesn’t stop there—the streaming behemoth also personalizes the artwork and imagery used to convey those titles to users.
The goal is to show you what you like, so if you’ve watched several movies starring Uma Thurman, you’d be likely to see Pulp Fiction art featuring her instead of co-stars John Travolta or Samuel L. Jackson.
To do so, Netflix uses a convolutional neural network that analyzes visual imagery. The company explains that they also rely on “contextual bandits,” which continually work to determine which artwork gets better engagement. Find out more about how to harness machine learning for artwork personalization here .
Myers-Briggs Personality Prediction
The Myers Briggs Type Indicator is a popular personality test that divides people into 16 different personality types across 4 axes. With this Kaggle dataset , you can evaluate the efficacy of the test and attempt to identify patterns related to personality type and writing style Each row in this dataset contains a person’s Myers-Briggs personality type along with examples of their writing.
The dataset could potentially be used to evaluate the validity of the Myers-Briggs test as it relates to the analysis, prediction, or categorization of human behavior. For example, you could apply machine learning techniques to examine the test’s ability to predict linguistic style and online behavior. Alternatively, try creating an algorithm that determines a subject’s personality type based on their writing.
YouTube Comment Analysis
If you want to analyze YouTube comments with natural language processing techniques, start by scraping your text data by leveraging a library like Youtube-Comment-Scraper-Python , which fetches YouTube video comments using browser automation.
With automated scraping, you’ll be able to focus your energy on exploratory data analysis, feature engineering, and other more advanced steps in the standard natural language processing workflow. Consider using your data to explore sentiment analysis, topic modeling, or word clouds.
Hate Speech Detection
Are you concerned about the growing prevalence of hate speech online? Try training a hate speech detection model using Python.
The United States does not have laws that prohibit hate speech, as the U.S. Supreme Court has ruled that criminalizing hate speech violates the constitutional right to free speech. However, the United Nations defines gate speech as communications that attack or use discriminatory language related to a person’s religion, ethnicity, race, gender, and other identity markers.
Using this definition of hate speech, you can develop a hate speech detection model using a dataset originally collected from Twitter. This interesting machine learning problem will revolve around sentiment classification. Learn more about creating a hate speech detection model with Python here .
If you need to jumpstart your project ideation, here are a few tips to put you on the right track.
Pick an Idea That Excites You
Create high-level concepts around your interests. If you’re passionate about fair housing, for example, learn more about how housing authorities in California are using AI to analyze and plan affordable housing strategies . Then consider building your own model using HUD or U.S. Census datasets.
If you’re a movie buff and aspire to work in the streaming space, peruse the Netflix Tech Blog for inspiration and try building different types of recommendation systems powered by collaborative filtering, content filtering, or a hybrid model.
Whatever topic you choose, solidify your most viable idea with a written proposal, which will serve as a blueprint to refer back to throughout the project.
Avoid Going Out of Scope
Scoping is the first stage of machine learning project planning, and offers an opportunity to settle on a data question, identify your objective, and select the machine learning solutions you will harness to solve your problem.
If you’re new to machine learning, focus on simple projects. Pick a small, succinctly-defined problem and research a lage, relevant data set to increase the odds that your project will generate a positive return on investment.
Test Your Hypothesis
In a machine learning context, hypothesis testing is conducted to confirm initial observations catalogued during data exploration and validate these assumptions for a desired significance level. First, you’ll model your hypothesis, and then you’ll select your hypothesis test type based on your predictor variable type (quantitative or categorical). Python is the easiest language for beginners who want to conduct hypothesis testing.
Implement the Results
Once you’ve reached all the desired outcomes, you’ll be ready to implement your project. This stage consists of several steps:
- Creating an API (application programming interface). This allows you to integrate your machine learning insights into the product.
- Record results on a single database. Collating your results will allow you to build upon them more easily.
- Embed the code. If you’re short on time, embedding the code is faster than an API.
Revise and Learn
When you’ve finished the project, evaluate your findings. Think about what happened, and why. What could you have done differently? As you gain experience, you will be able to learn from your mistakes over time.
Both simple and complex machine learning projects should be well-organized, properly documented, and presented in an impactful way. No matter your level of expertise, here are some concrete steps you can take to make your project shine.
How To Organize a Machine Learning Project
Properly organizing your machine learning project will boost productivity, ensure reproducibility, and make your project more accessible to other machine learning engineers and data scientists . When organizing your project, be sure to:
- Streamline your file structure. In your main project folder, create subfolders for notes, input files and data, src, models, and notebooks. If you’re working on GitHub, don’t forget to create a README.md file to introduce newcomers to your project.
- Manage data effectively. Use a directory structure and do not directly modify raw data. Be sure to check the consistency of your data and use a GNU make.
- Keep your code clean. Be sure to provide thorough documentation and organize your code into functional, annotated units.
How To Start a Machine Learning Project
Ready to get started on your project? Here’s how to begin:
- Identify your problem. What problem are you looking to solve, and why? Figure out which AI solution you will use to address the problem.
- Acquire your data. Download open-source data or try your hand at web scraping.
- Prepare your data. You may need to clean your data to eliminate unnecessary data or features. You may also need to transform your data, particularly if it is unstructured. Finally, you may also choose to conduct exploratory data analysis to look for patterns that might inform your project.
Once your data is prepared, you’ll be ready to develop your machine learning model.
How To Measure, Review, and Document an ML Project
To evaluate your machine learning project, you’ll need to use metrics to measure the performance of your model. The metrics you use will depend on your problem type. The performance of a classification model can be measured using metrics like accuracy, precision, and more. Specific evaluation metrics exist for regression, natural language processing, computer vision, deep learning, and other types of problems.
Before signing off on your project, you’ll need to review your work for quality assurance and reproducibility. To review your project, explain how you framed your question as a machine learning task and how you prepared your data. You should also compare your training, validation, and test metrics, and explain how you validated your model. Finally, you should note potential improvements and consider how you would deploy your model.
To share your work with others, you’ll also need to document your machine learning project. Your documentation should offer the information necessary to reproduce your work. It should clearly and succinctly outline the problem attacked, your proposed machine learning solution, and evidence of the solution’s success. Your project documentation should include:
- An executive summary of the project
- Context and background information about the problem
- A list of data sources
- Model documentation
- Validation performance results
- Appendix with source code
Once you’ve evaluated, reviewed, and documented your project, you’ll be able to show it to hiring managers.
How To Include a Machine Learning Project on Your Resume
Your machine learning resume should highlight what you can do for your employer. When adding a project to your resume, use it to demonstrate how you would create business value in your new role. cf
Frame your contributions to a project as accomplishments while using numbers and key metrics to convey your successes. Be sure to include the project title and a link to the project itself. After briefly describing the project, note the tools, programs, and skills used—and emphasize any that overlap with those detailed in the description of your desired role.
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Deep Learning Capstone Project Ideas
A set of statistical machine learning is referred to as deep learning to learn deep features in the hierarchical structure. ...
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A set of statistical machine learning is referred to as deep learning to learn deep features in the hierarchical structure. The main purpose of deep learning is to find the hidden elements of the raw data by passing over multiple layers. By the by, it also mimics the behaviour of the human brain by using the artificial neural networks (ANNs) concept. As a result, it is referred to as deep neural networks (DNNs) which are made up of the following layers,
- Input Layer
- Hidden Layer
- Output Layer
In this article, both scholars and deep learning projects for final year students can identify the research facts about the Deep Learning Capstone Project Ideas!!!
Moreover, the combination of deep learning and neural networks empowers modern technologies to function automatically under self-control mechanisms. As well, the architecture of a deep learning system is classified into three main types.
Categories of Deep Learning Architectures
- Hybrid Deep Learning Architectures
- Generative Architecture
- Discriminative Architectures
Our research team has sufficient knowledge on constructing all these architecture by doing small alterations for your handpicked deep learning projects for final year students . Besides, our research team has given you the unique functionalities of deep learning. We present to you how the deep learning model works for real-time scenarios. The baseline of the deep learning algorithm is as follows,
- Network Creation
- Data Collection
- Data Processing (Training)
- Data Analysis (Testing)
- Knowledge Acquisition (New Data Findings)
For your understanding, here we have given you the procedure of executing the deep learning model. By the by, the functionalities may vary based on the type of deep learning technique.
What are the fundamental steps in a deep learning algorithm?
- Create the network with an input layer, an output layer, the required number of nodes, and other entities
- Train the input data over the network
- Add one more hidden layer over the already learned network to create a new network
- Again, train the network that formed newly
- Replicate the same process and retrain the network
In addition, we have also given you the reason behind the tremendous growth of deep learning in the research and industrial sector. To emphasize the importance of deep learning, here we have compared deep learning with the existing traditional method of machine learning. We hope that you can realize the demand for deep learning capstone project ideas in current research. Once you connect with us, we are ready to share the latest collection of project ideas.
Why Deep Learning?
- In the existing method, the machine learning techniques manually extract the features, and also it has a fixed number of layers. This is solved by deep learning has no limitation over-processing and also automatically extracts the features with low human involvement.
- Further, it also uses a large amount of high-dimensional data which functions based on a neural network .
- For instance – image recognition and object tracking
How Deep Learning Works?
As a matter of fact, a technique of deep learning is greatly inspired by the human brain to imitate the functionalities of neurons of the brain. Also, it develops the same brain structure by interconnecting artificial neurons/perceptron’s over multiple network layers. For this purpose, it uses an artificial neural network concept. Then, it uses machine learning for self-learning of patterns. For instance: autonomous cars, face recognition, etc.
Matter of fact, machine learning act as a parent-class of deep learning. As well, it is popularly known for its algorithmic functions. Further, it also supports other complex mathematical problems. Since the learning ability of deep learning is capable to solve the problem by human-like thinking. Overall, deep learning is the most significant process to construct automated control systems. For your information, here we have given you the workflow of deep learning. Same as functionalities, the workflow also varies based on your application needs.
Step–by–Step Deep Learning Workflow
- Operating with messy data
- Data minimization and transformation
- Extracting important features
- Creating models like machine learning
- Optimizing parameters
- Validating models
- Interfacing with desktop apps
- Enterprise scaling systems using Java, Matlab, .Net, c, C++, etc.
- Embedding with hardware and devices
Before selecting the deep learning capstone project ideas, first, know the various kinds of deep learning networks. Since one type is different from other deep learning networks due to its functions and features. Also, it has different targets to achieve in deep learning-related projects. So, it is necessary to know the fundamental neural network types to identify your project type. Our developers have developed numerous deep learning applications in the following types and also still developing more. Therefore, we are capable to assist you with all these kinds of deep learning networks to achieve reliable results.
Types of Deep Learning Networks
- It is also very effective for pattern recognition
- It is one of the multilayer perception types
- For instance – Image recognition and Disease Detection
- It comprises more than 1 convolution layer with minimum parameters
- It is useful for splitting whole problems into several portions to solving
- It is a collection of small neural networks
- It has an individual target for every small network to work independently
- It takes a total of input weights as input to the input layer
- It is a fundamental type of neural network which controls workflow from the input layer to the output layer
- For instance – Face emotion recognition in computer vision
- It incorporates only I hidden layer to flow the data in 1 direction in the absence of the backpropagating technique
- It is used to manages low memory and to predict the outcome of network
- For instance – Chatbot for Text-to-Speech Conversion
- It has unlimited neural networks layers which transfer the output of one layer into the input of another layer
- It is completely connected with each node in the network
- For instance – Machine learning systems and speech recognition
- It has over 3 layers to categorize the non-linear data
- It is used in the case where there is a mismatch of length between input text and output text
- It is the combo of two recurrent neural networks
- It is comprised of an encoder and decoder for processing the system’s input and output
- It computes the relative distance between the center and any point and transfers output to the next layer
- For instance – Power Reestablishment Systems to escape from blackouts
- It has above 1 layer but prefers to have at least two layers
Deep Belief Net
Consider that you are using multiple-layer perception from the above list, then the problems of local minima and con-convex objective functions are solved through the deep belief network. As well, it replaces the place of classic deep learning which has interconnected multi-layers of latent variables deep learning capstone project ideas. Moreover, it is recognized as Restricted Boltzmann Machines (RBM) where hidden layers act as input for neighbouring layers of the network. In this, it creates a minimum visible layer to support the training set for independent training of neighbouring layers. The hidden variables are treated as observed variables to train layers of deep structure in deep learning projects for beginners . In specific, the training algorithm follows the below functionalities to train deep belief networks. Here, we have given you the implementation steps of the deep belief network.
- Take an account of the input vector
- Using input vector, train the restricted Boltzmann machine
- Acquire the weight matrix while training
- Using weight matrix, again train the lower layers of network
- Produce new input vector through mean activation (hidden units) or sampling
- Redo the same process until to reach upper layers of the network
This deep belief network is useful not only for multiple-layer perception but also for other purposes like acoustic modelling. Similarly, we provide you with keen assistance in other deep learning techniques and models. We hope that you are clear with fundamental information on deep learning.
Now, we can see the significant research areas of deep learning which are recommended by our experts. These areas are cross-checked by all means to present you with truthful information about master thesis deep learning . We found that the following areas have created the best impression among scholars by imposing new developments. Beyond this list of areas, we also support you in other emerging research areas. If you are curious to know the latest research ideas/topics of these areas then communicate with us.
Deep Learning Research Areas
- DL helps to identify the cancer cell patterns through training high-dimensional data
- DL helps to identify the object detection like traffic lights, pedestrians, stop signs, etc.
- Depends on the collected data, it insists vehicles take an automated decision for avoiding accidents.
- DL uses satellite sensors to detect objects for locating secure zones, insecure zones, areas of interest, etc.
- DL helps to automate the home appliances by hearing and speech translation. (i.e., home devices works based on voice instructions)
- DL aims to enhance the security features of an employee over insecure pieces of machinery
- Depends on the unsafe distance between employee and object, it automatically takes defensive measures
How to choose the best deep learning capstone project ideas?
For an effective capstone project idea, explore the recent year’s research papers to detect interesting topics from specific research areas. Eventually, a capstone project is intended to address the new research work. Further, it also provides the chance to explore deep learning projects for final year students ’ so far learned skills during the study.
Therefore, it is necessary to handpick topics that have a high degree of interest. Also, make sure that your selected Deep Learning Capstone Project Ideas has extended future scope for further studies. As well as, note down that the topic is gathered from the current research direction. On the whole, we are here to provide the best services on deep learning research and code development. We assure you that our project ideas are unique from various aspects like creativity, novelty, etc. This makes scholars/students choose us every time without any second choice. Also, we extend our service in paper writing and thesis writing along with publication support. So, make a bond with us to create remarkable research work in your deep learning research career.
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The Best 150 Capstone Project Topic Ideas
10 May 2022
❔What is a Capstone Project?
Capstone Project Ideas:
- 💾Computer Science
- 🎒High School Education
- 💻Information Technology
✅Capstone Writing: 10 Steps
The long path of research works ahead, and you can’t find any capstone project ideas that would be interesting and innovative? The task can seem even more challenging for you to feel all the responsibility of this first step. The top 150 capstone ideas presented below aim to make a choice not so effort-consuming.
With the list of the capstone project topics we've picked for you, you'll be covered in major subjects. Continue reading, and you'll get ideas for capstone projects in information technology, nursing, psychology, marketing, management, and more.
What is a Capstone Project?
Educational institutions use the capstone project to evaluate your understanding of the course on various parameters. For the students, the work on the project gives an excellent opportunity to demonstrate their presentation, problem-solving and soft skills. Capstone projects are normally used in the curriculum of colleges and schools. Also called a senior exhibition or a culminating project, these assignments are given to finish the academic course.
This assignment has several different objectives, among which are the following:
- to encourage independent planning,
- to learn to meet up deadlines,
- to practice a detailed analysis,
- to work in teams.
It's not that easy to pick the right capstone paper topic. The problem intensifies as each student or separate teams have to work on a single assignment which has to be unique. The best capstone project ideas may possibly run out. However, whatever topic you opt for, you’d better start your preparation and research on the subject as early as possible.
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Amazing Capstone Project Ideas for Nursing Course
Studying nursing is challenging, as it requires a prominent theoretical foundation and is fully practical at the same time. You should have to do thorough research and provide evidence for your ideas, but what to start with? The preparation for your capstone project in nursing won’t be so overwhelming if you make use of these capstone title ideas:
- Innovation and Improvement in Nursing
- Vaccination Chart Creation
- The Role of Nurses in Today's Society
- Shortage in Nursing and Its Effects on Healthcare
- Evidential Practices and Their Promotion in Nursing
- Global Changes in the Approach to Vaccination
- Top Emergency Practices
- Preventive Interventions for ADHD
- Quality of Nursing and Hospital Personnel Shifts: The Interrelation
- Ways to Prevent Sexually Transmitted Diseases
- Brand New Approaches in Diagnostics in the Nursing Field
- Diabetes Mellitus in Young Adults: Prevention and Treatment
- Healthcare in Ambulances: Methods of Improvement
- Postpartum Depression Therapy
- The Ways to Carry a Healthy Baby
Attractive Computer Science Capstone Project Ideas
Computer science is so rapidly developing that you might easily get lost in the new trends in the sphere. Gaming and internet security, machine learning and computer forensics, artificial intelligence, and database development – you first have to settle down on something. Check the topics for the capstone project examples below to pick one. Decide how deeply you will research the topic and define how wide or narrow the sphere of your investigation will be.
- Cybersecurity: Threats and Elimination Ways
- Data Mining in Commerce: Its Role and Perspectives
- Programming Languages Evolution
- Social Media Usage: How Safe It Is?
- Classification of Images
- Implementation of Artificial Intelligence in Insurance Cost Prediction
- Key Security Concerns of Internet Banking
- SaaS Technologies of the Modern Time
- Evolvement of Mobile Gaming and Mobile Gambling
- The Role of Cloud Computing and IoT in Modern Times
- Chatbots and Their Role in Modern Customer Support
- Computer Learning Hits and Misses
- Digitalization of Education
- Artificial Intelligence in Education: Perspectives
- Software Quality Control: Top Modern Practices
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Several High School Education Capstone Project Ideas for Inspiration
High school education is a transit point in professional education and the most valuable period for personal soft skills development. No wonder that the list of capstone project ideas in high school education involves rather various topics. They may range from local startup analysis and engineer’s career path to bullying problems. It’s up to you to use the chosen statement as the ready capstone project title or just an idea for future development.
- A Small Enterprise Business Plan
- Advantages and Disadvantages of Virtual Learning in Schools
- Space Tourism: The Start and Development
- Pros and Cons of Uniforms and Dress Codes
- What is Cyberbullying and How to Reduce It
- Becoming a Doctor: Find Your Way
- Career in Sports: Pros and Cons
- How to Eliminate the Risks of Peer Pressure
- Ensuring Better Behaviours in Classroom
- Cutting-Edge Technologies: NASA versus SpaceX
- The Reverse Side of Shyness
- Stress in High School and the Ways to Minimize It
- How to Bring Up a Leader
- Outdated Education Practices
- Learning Disabilities: What to Pay Attention to in Children’s Development
Capstone Project Topics in Information Technology – Search for Your Best
Information technology is a separate area developed on the basis of computer science, and it might be challenging to capture the differences between them. If you hesitate about what to start with – use the following topics for capstone project as the starting point for your capstone research topics.
- Types of Databases in Information Systems
- Voice Recognition Technology and Its Benefits
- The Perspectives of Cloud Computing
- Security Issues of VPN Usage
- Censorship in Internet Worldwide
- Problems of Safe and Secure Internet Environment
- The Cryptocurrency Market: What Are the Development Paths?
- Analytics in the Oil and Gas Industry: The Benefits of Big Data Utilization
- Procedures, Strengths and Weaknesses in Data Mining
- Networking Protocols: Safety Evaluation
- Implementation of Smart Systems in Parking
- Workplace Agile Methodology
- Manual Testing vs. Automated Testing
- Programming Algorithms and the Differences Between Them
- Strengths and Weaknesses of Cybersecurity
Psychology Capstone Project Ideas
Society shows increasing attention to mental health. The range of issues that influence human psychology is vast, and the choice may be difficult. You’ll find simple capstone project ideas to settle on in the following list.
- The Impact of Abortion on Mental Health
- Bipolar Disorder and Its Overall Effects on the Life Quality
- How Gender Influences Depression
- Inherited and Environmental Effects on Hyperactive Children
- The Impact of Culture on Psychology
- How Sleep Quality Influences the Work Performance
- Long- and Short-Term Memory: The Comparison
- Studying Schizophrenia
- Terrorist’s Psychology: Comprehension and Treatment
- The Reasons for Suicidal Behaviour
- Aggression in Movies and Games and Its Effects on Teenagers
- Military Psychology: Its Methods and Outcomes
- The Reasons for Criminal Behavior: A Psychology Perspective
- Psychological Assessment of Juvenile Sex Offenders
- Do Colours Affect The Brain?
Stuck with finding the right title?
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Capstone Project Ideas for Management Course
Studying management means dealing with the most varied spheres of life, problem-solving in different business areas, and evaluating risks. The challenge starts when you select the appropriate topic for your capstone project. Let the following list help you come up with your ideas.
- Innovative Approaches in Management in Different Industries
- Analyzing Hotels Customer Service
- Project Manager: Profile Evaluation
- Crisis Management in Small Business Enterprises
- Interrelation Between Corporate Strategies and Their Capital Structures
- How to Develop an Efficient Corporate Strategy
- The Reasons For Under-Representation of Managing Women
- Ways to Create a Powerful Public Relations Strategy
- The Increasing Role of Technology in Management
- Fresh Trends in E-Commerce Management
- Political Campaigns Project Management
- The Risk Management Importance
- Key Principles in the Management of Supply Chains
- Relations with Suppliers in Business Management
- Business Management: Globalization Impact
Capstone Project Ideas for Your Marketing Course
Marketing aims to make the business attractive to the customer and client-oriented. The variety of easy capstone project ideas below gives you the start for your research work.
- How to Maximize Customer Engagement
- Real Businesses Top Content Strategies
- Creation of Brand Awareness in Online Environments
- The Efficiency of Blogs in Traffic Generation
- Marketing Strategies in B2B and B2C
- Marketing and Globalization
- Traditional Marketing and Online Marketing: Distinguishing Features
- How Loyalty Programs Influence Customers
- The Principles of E-Commerce Marketing
- Brand Value Building Strategies
- Personnel Metrics in Marketing
- Social Media as Marketing Tools
- Advertising Campaigns: The Importance of Jingles
- How to Improve Marketing Channels
- Habitual Buying Behaviours of Customers
Best Capstone Engineering Project Ideas
It’s difficult to find a more varied discipline than engineering. If you study it – you already know your specialization and occupational interest, but the list of ideas below can be helpful.
- How to Make a Self-Flying Robot
- How to Make Robotic Arm
- Biomass Fuelled Water Heater
- Geological Data: Transmission and Storage
- Uphill Wheelchairs: The Use and Development
- Types of Pollution Monitoring Systems
- Operation Principles of Solar Panels
- Developing a Playground for Children with Disabilities
- The Car with a Remote-Control
- Self-Driving Cars: Future or Fantasy?
- The Perspectives of Stair-Climbing Wheelchair
- Mechanisms of Motorized Chains
- How to Build a Car Engine
- Electric Vehicles are Environment-Friendly: Myth or Reality?
- The Use of Engineering Advancements in Agriculture
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Capstone Project Ideas for MBA
Here you might read some senior capstone project ideas to help you with your MBA assignment.
- Management Strategies for Developing Countries Businesses
- New App Market Analysis
- Corporate Downsizing and the Following Re-Organization
- How to Make a Business Plan for a Start-Up
- Relationships with Stakeholders
- Small Teams: Culture and Conflict
- Organization Managing Diversity
- What to Pay Attention to in Business Outsourcing
- Business Management and Globalization
- The Most Recent HR Management Principles
- Dealing with Conflicts in Large Companies
- Culturally Differentiated Approaches in Management
- Ethical Principles in Top-Tier Management
- Corporate Strategy Design
- Risk Management and Large Businesses
Capstone Project Ideas for an Accounting Course
Try these ideas for your Capstone Project in Accounting – and get the best result possible.
- How Popular Accounting Theories Developed
- Fixed Assets Accounting System
- Accounting Principles in Information Systems
- Interrelation Between Accounting and Ethical Decision-Making
- Ways to Minimize a Company’s Tax Liabilities
- Tax Evasion and Accounting: Key Principles
- Auditing Firm Accounting Procedures
- A New Accounting Theory Development
- Accounting Software
- Top Three World Recessions
- Accounting Methods in Proprietorship
- Accounting Standards Globally and Locally
- Personal Finance and the Recession Effect
- Company Accounting: Managerial Principles and Functions
- Payroll Management Systems
Capstone Writing: 10 Essential Steps
Be it a senior capstone project of a high school pupil or the one for college, you follow these ten steps. This will ensure you’ll create a powerful capstone paper in the outcome and get the best grade:
- One of the tips to choose a topic that your professors would be interested in is picking a subject in the course of your classes. Make notes during the term and you will definitely encounter an appropriate topic.
- Opt for a precise topic rather than a general one. This concerns especially business subjects.
- Have your capstone project topic approved by your professor.
- Conduct a thorough information search before developing a structure.
- Don’t hesitate to do surveys; they can provide extra points.
- Schedule your time correctly, ensuring a large enough time gap for unpredictable needs.
- Never avoid proofreading – this is the last but not least step before submission.
- Stick up to the topic and logical structure of your work.
- Get prepared to present your project to the audience, learn all the essential points, and stay confident.
- Accept feedback open-mindedly from your teacher as well as your peers.
Preparation of a powerful capstone project involves both selection of an exciting topic and its in-depth examination. If you are interested in the topic, you'll be able to demonstrate to your professor a deep insight into the subject. The lists of ideas above will inspire you and prepare you for the successful completion of your project. Don’t hesitate to try them now!
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How To Write A Capstone Project Outline: Steps and Example
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Best capstone project topics on machine learning (ml).
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1. Machine Learning (Career Building Course)
2. Fraud Detection using Machine Learning
3. Machine Learning using Python
4. Movie Recommendation using ML
5. Handwritten Digits Recognition using ML
6. Machine Learning Training & Internship
7. Brain Tumor Detection using Deep Learning
Machine learning is an innovative technology which teaches the machine(computer) on particular tasks using certain algorithms to make the process faster with minimal human intervention. In this article, we will also discuss some good capstone projects on machine learning.
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What is a Capstone Project?
Usually, we see students exhibiting Capstone Projects during their final year of study. Also known as capstone assignments or capstone courses, it is that period of time where the student must devote 1 or 2 semesters in their final year where he/she spends time doing researches, gathering information and structuring into a research paper (can be involved) or a project idea by choosing one particular field of interest. These kind of assignments are the skills and knowledge gained throughout their years of study. We as humans are more of problems creators than solvers. And capstone courses will help you pave the way into a budding problem solver out of your own interests.
In this article, we will focus on capstone projects to one particular field that has been blooming and constantly on the run to expand, and that is Machine Learning.
Machine Learning is what we call as a relative to AI (Artificial Intelligence) and as the name suggests, it is a process of in-depth learning of computer systems and how they can work independently without much of human assistance or interventions, such as identifying patterns, completing tasks and so on. Now hold on to that as we get going with capstone project ideas on this domain.
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Good capstone project topics on Machine Learning
1. Fraud detection using Machine Learning
Credit card frauds are still on the rise day by day. Although there have been systems developed to detect such fraud transactions, the count doesn’t number down. If you think this is a project you’d like to work on, then maybe your skills and knowledge would finally bring about an optimal solution to this issue, never know.
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2. Recognition of Handwritten digits
How blown away would you be to find a non-humanoid structure who is able to figure out the numbers that are handwritten by you? Sounds pretty simple and amazing, but this capstone project requires a lot of data, intellect and time to devote. And about 2 semesters can surely give you all the ample time to spend on working on this project. Once you understand how you’ve been able to implement Machine Learning into this capstone project, you will know how systems or cameras can identify numbers on a number plate, numbers on a bank check etc., since these systems use the same concept.
Discover more about this capstone project
3. Recognizing duplicate images
Most of the time, people end up finding pictures from Google, because there’s a ton of variety. But in that variety, there might be several other tons of pictures that are duplicates- could be because they were cropped, resized, or watermarked etc. One such solution to recognizing such images would be to train a system to identify duplicates, keeping the originals untouched. This way, your system can save up on space and also keep the originals intact so you can have your own additions to them.
4. Damaged Photo restoration
We all know how well we’ve been protecting our planet to an extent where there has been tremendous climate changes and an increase in temperatures that obviously seem impossible to revert. Photographs are one of the victims to these changes and they eventually lose their vibrancy and colour. For instance, even if water comes in contact with pictures, they can easily get inked off and you cannot really cherish dull old memories. One thing you could possibly do is to have a system that you can train to perform network mapping tasks to retrieve original pictures by restoring their colour channels. Guess you can finally cherish those old memories!
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5. Music recommendation
Now, this is an interesting topic you can work on. I mean, who doesn’t love music? Presuming you love music as much as I do, I think you will love working on this capstone project because you will learn so much on how your system will recommend you songs based on your taste and playlists that are on repeat. This kind of concept is applied for movie recommendations as well, so sit back and pick a direction.
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6. Social Sentiments Analysis
This capstone project is a huge deal in the social media market. Right from platforms like Instagram, that is currently gushing with every human’s opinions and emotions, sentiment analysis plays a very significant role to dealers, marketers, influencers of brands and other marketing ways. With the help of Natural Language Processing (NLP), you will learn how a system helps brand reputations and keeps the business running on these social platforms. This machine learning project is more inclined towards data mining and analyzes consumer opinions to generate accurate results.
Learn more about this project
Also, check out the below list for more capstone projects:
- Students Performance Prediction using Machine Learning
- Speech Emotion Recognition
- Detecting Parkinson's Disease using Machine Learning
- Chatbox Machine Learning project
- Image Caption Generator
- Customer Segmentation
- AI-based Voice Assistant
- Movie Ticket Pricing system using Machine Learning
- Object detection using Machine Learning
- Coronavirus outbreak prediction project using Machine Learning
- Breast Cancer Prediction using Machine Learning
The ocean is vast, and so is Machine Learning. There is still a lot of blooming in this field of tech that is hard to wipe out anytime soon. If you’re looking for similar projects to work on, SkyFi knows how to surprise you. Also, if you run out of ideas for any project of any field of tech, we are always here to help you out.
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Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas
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Awesome Deep Learning Project Ideas
A curated list of practical deep learning and machine learning project ideas
- Relevant to both the academia and industry
- Ranges from beginner friendly to research projects
LLM Apps - Project ideas unlocked by use of Large Language Models, specially text to text -- note that a lot of the text to text ideas can also be buit a lot better with LLMs now!
Text - With some topics about Natural language processing
Forecasting - Most of the topics in this section is about Time Series and similar forecasting challenges
Vision - With topics about image and video processing
Music and Audio - These topics are about combining ideas from language and audio to understand music
Knowledge Base QA aka Answer Engines
- Can use this over Video Subtitles to search and QA over videos as well, by mapping back to source
- Take specific questions which the user might have about a large text dataset e.g. a novel or book and include that in your summary of the piece
- Pay attention to specific entities and retell the events which happen in a story with attention to that character
Text to Code/SQL
- Use code understanding to convert use query to SQL or another executable programming language, including Domain Specific Languages
- Here is an example of the same: qabot
Autonomous Tagging of StackOverflow Questions
- Make a multi-label classification system that automatically assigns tags for questions posted on a forum such as StackOverflow or Quora.
- Dataset: StackLite or 10% sample
- Identify keywords from millions of questions
- Dataset: StackOverflow question samples by Facebook
- Multi-label classification of printed media articles to topics
- Dataset: Greek Media monitoring multi-label classification
Natural Language Understanding
Sentence to Sentence semantic similarity
- Can you identify question pairs that have the same intent or meaning?
- Dataset: Quora question pairs with similar questions marked
Fight online abuse
- Can you confidently and accurately tell whether a particular comment is abusive?
- Dataset: Toxic comments on Kaggle
Open Domain question answering
- Can you build a bot which answers questions according to the student's age or her curriculum?
- Facebook's FAIR is built in a similar way for Wikipedia.
- Dataset: NCERT books for K-12/school students in India, NarrativeQA by Google DeepMind and SQuAD by Stanford
Automatic text summarization
- Can you create a summary with the major points of the original document?
- Abstractive (write your own summary) and Extractive (select pieces of text from original) are two popular approaches
- Dataset: CNN and DailyMail News Pieces by Google DeepMind
- Generate plausible new text which looks like some other text
- Obama Speeches? For instance, you can create a bot which writes some new speeches in Obama's style
- Trump Bot? Or a Twitter bot which mimics @realDonaldTrump
- Narendra Modi bot saying " doston "? Start by scrapping off his Hindi speeches from his personal website
- Example Dataset: English Transcript of Modi speeches
Check mlm/blog for some hints.
- Do Twitter Sentiment Analysis on tweets sorted by geography and timestamp.
- Dataset: Tweets sentiment tagged by humans
Univariate Time Series Forecasting
- How much will it rain this year?
- Dataset: 45 years of rainfall data
Multi-variate Time Series Forecasting
- How polluted will your town's air be? Pollution Level Forecasting
- Dataset: Air Quality dataset
- Find a short term forecast on electricity consumption of a single home
- Dataset: Electricity consumption of a household
Predict Blood Donation
- We're interested in predicting if a blood donor will donate within a given time window.
- More on the problem statement at Driven Data .
- Dataset: UCI ML Datasets Repo
- Can you predict the rating a user will give on a movie?
- Do this using the movies that user has rated in the past, as well as the ratings similar users have given similar movies.
- Dataset: Netflix Prize and MovieLens Datasets
Search + Recommendation System
- Predict which Xbox game a visitor will be most interested in based on their search query
- Dataset: BestBuy
Can you predict Influencers in the Social Network?
- How can you predict social influencers?
- Dataset: PeerIndex
- Object recognition or image classification task is how Deep Learning shot up to it's present-day resurgence
- MS COCO is the modern replacement to the ImageNet challenge
- MNIST Handwritten Digit Classification Challenge is the classic entry point
- Character recognition (digits) is the good old Optical Character Recognition problem
- Bird Species Identification from an Image using the Caltech-UCSD Birds dataset dataset
- Dataset: MICCAI Machine Learning Challenge aka MLC 2014
- Dataset: MOAA Right Whale
- Dataset: State Farm Distracted Driver Detection on Kaggle
Bone X-Ray competition
- Can you identify if a hand is broken from a X-ray radiographs automatically with better than human performance?
- Stanford's Bone XRay Deep Learning Competition with MURA Dataset
- Can you caption/explain the photo a way human would?
- Dataset: MS COCO
Image Segmentation/Object Detection
- Can you extract an object of interest from an image?
- Dataset: MS COCO , Carvana Image Masking Challenge on Kaggle
Large-Scale Video Understanding
- Can you produce the best video tag predictions?
- Dataset: YouTube 8M
- Can you select the semantically relevant/important parts from the video?
- Example: Fast-Forward Video Based on Semantic Extraction
- Dataset: Unaware of any standard dataset or agreed upon metrics? I think YouTube 8M might be good starting point.
- Can you recompose images in the style of other images?
- Dataset: fzliu on GitHub shared target and source images with results
- Can you detect if someone is sick from their chest XRay? Or guess their radiology report?
- Dataset: MIMIC-CXR at Physionet
Clinical Diagnostics: Image Identification, classification & segmentation
- Can you help build an open source software for lung cancer detection to help radiologists?
- Link: Concept to clinic challenge on DrivenData
Satellite Imagery Processing for Socioeconomic Analysis
- Can you estimate the standard of living or energy consumption of a place from night time satellite imagery?
- Reference for Project details: Stanford Poverty Estimation Project
Satellite Imagery Processing for Automated Tagging
- Can you automatically tag satellite images with human features such as buildings, roads, waterways and so on?
- Help free the manual effort in tagging satellite imagery: Kaggle Dataset by DSTL, UK
Music/Audio Recommendation Systems
- Can you tell if two songs are similar using their sound or lyrics?
- Dataset: Million Songs Dataset and it's 1% sample.
- Example: Anusha et al
Music Genre recognition using neural networks
- Can you identify the musical genre using their spectrograms or other sound information?
- Datasets: FMA or GTZAN on Keras
- Get started with Librosa for feature extraction
Can I use the ideas here for my thesis? Yes, totally! I'd love to know how it went.
Do you have any advice before I start my project? Advice for Short Term Machine Learning Projects by Tim R. is a pretty good starting point!
How can I add my ideas here? Just send a pull request and we'll discuss?
Hey, something is wrong here! Yikes, I am sorry. Please tell me by raising a GitHub issue .
I'll fix it as soon as possible.
Problems are motivated by the ones shared at:
- CMU Machine Learning
- Stanford CS229 Machine Learning Projects
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15 NLP Projects Ideas for Beginners With Source Code for 2023
Explore some simple, interesting and advanced NLP Projects ideas with source code that you can practice to become an NLP engineer. Last Updated: 02 Feb 2023
In this blog, explore a diverse list of interesting NLP projects ideas, from simple NLP projects for beginners to advanced NLP projects for professionals that will help master NLP skills.
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Last Updated : 2023-02-27 18:00:27
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As per the Future of Jobs Report released by the World Economic Forum in October 2020, humans and machines will be spending an equal amount of time on current tasks in the companies, by 2025. The report has also revealed that about 40% of the employees will be required to reskill and 94% of the business leaders expect the workers to invest in learning new skills. They are showing great interest in adopting cloud computing along with other technologies like non-human robots, artificial intelligence (AI), and encryption. All the numbers presented above suggest that there will be a huge demand for people who are skilled at implementing AI-based technologies. One such sub-domain of AI that is gradually making its mark in the tech world is Natural Language Processing (NLP). You can easily appreciate this fact if you start recalling that the number of websites or mobile apps, you’re visiting every day, are using NLP-based bots to offer customer support.
As we already revealed in our Machine Learning NLP Interview Questions with Answers in 2021 blog, a quick search on LinkedIn shows about 20,000+ results for NLP-related jobs. Thus, now is a good time to dive into the world of NLP and if you want to know what skills are required for an NLP engineer, check out the list that we have prepared below.
Table of Contents
Skills required to become an nlp engineer, interesting nlp projects for beginners, nlp projects idea #1 sentiment analysis, nlp projects idea #2 conversational bots: chatbots, nlp projects idea #3 topic identification, nlp projects idea #4 summary writer, nlp projects idea #5 grammar autocorrector, nlp projects idea #6 spam classification.
- NLP Projects Idea #7 Text Processing and Classification
Simple NLP Projects
Nlp projects idea #1 sentence autocomplete, nlp projects idea #2 market basket analysis, nlp projects idea #3 automatic questions tagging system, nlp projects idea #4 resume parsing system, nlp open source projects, nlp projects idea #1 recognising similar texts, nlp projects idea #2 inappropriate comments scanner, advanced nlp projects, nlp projects idea #1 language identifier, nlp projects idea #2 image-caption generator, nlp projects idea #3 homework helper.
Comfortable with implementing NLP techniques in at least one of the popular deep learning frameworks (PyTorch, Tensorflow , etc.).
Good knowledge of commonly used machine learning and deep learning algorithms .
Strong understanding of statistical techniques used to quantify the results of NLP algorithms.
Hands-on experience with cloud-based platforms such AWS, Azure.
Past experience with utilizing NLP algorithms is considered an added advantage.
Utilize natural language data to draw insightful conclusions that can lead to business growth.
Design NLP-based applications to solve customer needs.
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15 NLP Projects Ideas to Practice
Apart from the skills mentioned above, recruiters often ask applicants to showcase their Project portfolios. They do so in order to have an idea of how good you are at implementing NLP algorithms and how well you can scale them up for their business. To help you in overcoming this challenge, we have prepared an informative list of NLP Projects. And to make your browsing hassle-free, we have divided the projects into the following four categories:
NLP Open-source Projects
So, go ahead, pick your category and try implementing your favorite projects today!
In this section of our NLP Projects blog, you will find NLP-based projects that are beginner-friendly. If you are new to NLP, then these NLP full projects for beginners will give you a fair idea of how real-life NLP projects are designed and implemented.
This is one of the most popular NLP projects that you will find in the bucket of almost every NLP Research Engineer. The reason for its popularity is that it is widely used by companies to monitor the review of their product through customer feedback. If the review is mostly positive, the companies get an idea that they are on the right track. And, if the sentiment of the reviews concluded using this NLP Project are mostly negative then, the company can take steps to improve their product.
Method: The first step to start designing the Sentiment Analysis system would involve performing EDA over textual data. After that, you will have to use text data processing methods to extract relevant information from the data and remove gibberish. The next step would be to use significant words in the reviews to analyze the sentiment of the reviewer. Through this project, you can learn about the TF-IDF method, Markov Chain concept, and feature engineering . If you want a detailed solution for this project, check out this project from our repository: Ecommerce product reviews - Pairwise ranking and sentiment analysis . Recommended Reading: How to do text classification?
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As we mentioned at the beginning of this blog, most tech companies are now utilizing conversational bots, called Chatbots to interact with their customers and resolve their issues. This is a very good way of saving time for both customers and companies. The users are guided to first enter all the details that the bots ask for and only if there is a need for human intervention, the customers are connected with a customer care executive.
Method: In this project, you will learn how to use the NLTK library in Python for text classification and text preprocessing. You will also get to explore how Tokenization, lemmatization , and Parts-of-Speech tagging are implemented in Python. Through this project, you will get accustomed to models like Bag-of-words, Decision tree, and Naive Bayes. To look at a more detailed solution to the solution of this project, check out the chatbot example application using python - text classification using nltk .
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This is a very basic NLP Project which expects you to use NLP algorithms to understand them in depth. The task is to have a document and use relevant algorithms to label the document with an appropriate topic. A good application of this NLP project in the real world is using this NLP project to label customer reviews. The companies can then use the topics of the customer reviews to understand where the improvements should be done on priority.
Method: This project will introduce you to methods of handling textual data and using regex You will understand how to convert textual data into vectors through methods like TF-IDF and Count vectorizer. You will also learn how to use unsupervised machine learning algorithms for grouping similar reviews together. To know more, read Topic Modeling using K Means Clustering .
Recommended Reading: K-means Clustering Tutorial-Machine Learning
We are all living in a fast-paced world where everything is served right after a click of a button. People now want everything to be given to them at a fast speed. And that is why short news articles are becoming more popular than long news articles. One such instance of this is the popularity of the Inshorts mobile application that summarizes the lengthy news articles into just 60 words. And the app is able to achieve this by using NLP algorithms for text summarization.
Method: This NLP Project will help you in understanding how to use NLP algorithms for ranking various sentences in the document based on their significance. You will have to use algorithms like Cosine Similarity to understand which sentences in the given document are more relevant and will form the part of the summary.
Gone are the days when one will have to use Microsoft Word for grammar check. Nowadays, most text editors offer the option of Grammar Auto Correction. There is even a website called Grammarly that is gradually becoming popular among writers. The website offers not only the option to correct the grammar mistakes of the given text but also suggests how sentences in it can be made more appealing and engaging. All this has become possible thanks to the AI subdomain, Natural Language Processing.
Method: This NLP project will require you to not use advanced machine learning algorithms. You should train your algorithms with a large dataset of texts that are widely appreciated for the use of correct grammar. For training, it’s a must that you perform necessary NLP techniques like Lemmatization, Removal of stop words/ irrelevant words, Removal of punctuations, etc.
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Recall those not-so-good old days of using emails where we used to receive so many junk emails and very few relevant emails. We have come so far from those days, haven’t we? A good amount of credit for this transformation goes to NLP. Using the NLP algorithms, email service providing systems can identify spam emails easily which helps their user base in saving time by avoiding unnecessary emails in their inbox.
Method: For this NLP project, you will have to collect a dataset of emails and then use the body of the email for training your algorithm. You can use deep learning or machine algorithms to achieve this but as a beginner, we’d suggest you stick to machine learning algorithms as they are relatively easy to understand.
NLP Projects Idea #7 Text Processing and Classification
For newbies in machine learning, understanding Natural Language Processing (NLP) can be quite difficult. To smoothly understand NLP, one must try out simple projects first and gradually raise the bar of difficulty. So, if you are a beginner who is on the lookout for a simple and beginner-friendly NLP project, we recommend you start with this one.
Project Objective: Understand NLP from scratch by working on the simple problem of text classification.
Learnings from the Project: Your first takeaway from this project will be data visualization and data preprocessing. Additionally, you will learn about Stopwords, Tokenisation, Stemming using Lancaster Stemmer, N-grams model, TF-IDF. You will also get to explore the implementation of the logistic regression model on a textual dataset.
Tech Stack: Language: Python, Libraries: pandas, seaborn, matplotlib, sklearn, nltk
Access the full solution to NLP Project for Beginners on Text Processing and Classification
This heading has those sample NLP project ideas that are not as effortless as the ones mentioned in the previous section. For beginners in NLP who are looking for a challenging task to test their skills, these cool NLP projects will be a good starting point. Also, you can use these NLP project ideas for your graduate class NLP projects.
- 15 Computer Vision Project Ideas for Beginners in 2021
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- Best NLP Books- What Data Scientists Must Read in 2021?
- Access Job Recommendation System Project with Source Code
This is an exciting NLP project that you can add to your NLP Projects portfolio for you would have observed its applications almost every day. Wondering where? Well, it’s simple, when you’re typing messages on a chatting application like WhatsApp. We all find those suggestions that allow us to complete our sentences effortlessly. Turns out, it isn’t that difficult to make your own Sentence Autocomplete application using NLP.
Method: This is the perfect NLP project for understanding the n-gram model and its implementation in Python. You can use various deep learning algorithms like RNNs, LSTM, Bi LSTMs, Encoder-and-decode r for the implementation of this project. Of course, you will first have to use basic NLP methods to make your data suitable for the above algorithms.
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Every time you go out shopping for groceries in a supermarket, you must have noticed a shelf containing chocolates, candies, etc. are placed near the billing counter. It is a very smart and calculated decision by the supermarkets to place that shelf there. Most people resist buying a lot of unnecessary items when they enter the supermarket but the willpower eventually decays as they reach the billing counter. Another reason for the placement of the chocolates can be that people have to wait at the billing counter, thus, they are somewhat forced to look at candies and be lured into buying them. It is thus important for stores to analyze the products their customers purchased/customers’ baskets to know how they can generate more profit.
Method: This NLP project will give you a great idea about how Market Basket Analysis is relevant for companies. You will understand different association rules and learn the apriori and the Fp Growth algorithm. You will also get to know about univariate and bivariate analysis. To know more about this NLP project, refer to Market basket analysis using apriori and fpgrowth algorithm tutorial example implementation .
Sites that are specifically designed to have questions and answers for their users like Quora and Stackoverflow often request their users to submit five words along with the question so that they can be categorized easily. But, sometimes users provide wrong tags which makes it difficult for other users to navigate through. Thus, they require an automatic question tagging system that can automatically identify correct and relevant tags for a question submitted by the user.
Method: For implementing this project you can use the dataset StackSample . It is a huge dataset that has three files: Answers, Questions, and Tags. All three files are in CSV format so you can use the Python Pandas library to perform the necessary analysis. The three files are connected by the column ‘id’ which is unique for each question. Each question has at least three tags and your task is to predict these tags using questions and answers.
A resume parsing system is an application that takes resumes of the candidates of a company as input and attempts to categorize them after going through the text in it thoroughly. This application, if implemented correctly, can save HR and their companies a lot of their precious time which they can use for something more productive.
Method: This parsing system can be built using NLP techniques and a generic machine learning framework. Through this NLP project, you will understand Optical Character Recognition and conversion of JSON to Spacy format. As resumes are mostly submitted in PDF format, you will get to learn how text is extracted from PDFs. Access the source code for Resume Parsing, refer to Implementing a resume parsing application.
This heading has the list of NLP projects that you can work on easily as the datasets for them are open-source.
This NLP project is a must for any NLP enthusiast. It was launched as a challenge on Kaggle about 4 years ago. If you have ever visited the Quora website, you would have noticed sometimes, two questions on the website have the same meaning but different answers. This creates a problem as the website wants its readers to have access to all answers that are relevant to their questions. In order to solve this problem, Quora launched the Quora Question Pairs Challenge and asked the Data Scientists to come with a solution for identifying questions that have a similar intent. The idea is to present all the answers to their readers for all the questions that may look different but have the same intent. Method: In this NLP Project, you can use bar plots and histograms to visualize textual data before using any machine learning algorithms on it. You will have to perform lemmatization, remove stop words, convert text to numbers using vectorization techniques. After that, you should use various machine learning algorithms like logistic regression, gradient boosting, random forest, and grid search CV for tuning the hyperparameters. To know the step-by-step solution for this, click NLP Projects - Kaggle Quora Question Pairs Solution .
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The twenty-first century is the age of social media. On one hand, many small businesses are benefiting and on the other, there is also a dark side to it. Because of social media, people are becoming aware of ideas that they are not used to. While few take it positively and make efforts to get accustomed to it, many start taking it in the wrong direction and start spreading toxic words. Thus, many social media applications take necessary steps to remove such comments to predict their users and they do this by using NLP techniques.
Method: The dataset for this project is freely available on Kaggle . You can use this dataset to classify the comments as toxic and non-toxic. For this project, you will have to first use textual data preprocessing techniques. After that, you must perform basic NLP methods like TF-IDF of converting textual data into numbers and then use machine learning algorithms to label the comments.
If you are a pro at NLP, then the projects below are perfect for you. They are challenging and equally interesting projects that will allow you to further develop your NLP skills.
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How often have you traveled to a city where you were excited to know what languages they speak? That’s such a common thing. To discover a language, you don’t always have to travel to that city, you might even come across a document while browsing through websites on the Internet or going through books in your library and may have the curiosity to know which language it is. This NLP Project is all about quenching your curiosity only.
Method: This project will involve using the Language Detection dataset for training your machine learning/deep learning algorithm. This dataset has two columns: text and language. After performing text preprocessing methods, you can use your preferred algorithm to predict the correct target variable of language for a given text. If you want to implement this NLP project in Python, we suggest you use libraries like Pandas, Numpy, Seaborn, NLTK, and Matplotlib.
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Consider you are given a system and asked to describe it. It sounds like a simple task but for someone with weak eyesight or no eyesight, it would be difficult. And that is why designing a system that can provide a description for images would be a great help to them.
Method: This advanced NLP project is a slightly complex one but is equally interesting. One must have a fair idea of deep learning algorithms and image processing techniques as well to implement this project. So, if you haven’t tried them yet, this project will motivate you to understand them. You will have to first use image processing and deep learning algorithms to label objects in the image and then convert that information into relevant sentences through NLP methods.
Recommended Reading: Top 10 Deep Learning Algorithms in Machine Learning
This is a very cool NLP project for all the parents out there who struggle with helping their children in completing complicated tasks assigned as homework to their kids. The reason is simple : they feel like they’re too old for it and have forgotten most of the things. But dear parents don’t worry, NLP is here to help. By designing a simple NLP-based app, you can help your kids with their homework.
Method: For this NLP based project, you can use pdfs by NCERT or by any other freely available publication house as your dataset. You can implement NLP methods to analyze the data and then use specific machine learning or deep learning algorithms to find answers/relevant text to the questions asked by the user.
If you enjoyed reading about these NLP project ideas and are looking for more NLP Data Science projects ideas with solutions then check out our repository: Top NLP Projects | Natural Language Processing Projects .
What are NLP tasks?
NLP comprises multiple tasks that allow you to investigate and extract information from unstructured content. These tasks include Stemming, Lemmatisation, Word Embeddings, Part-of-Speech Tagging, Named Entity Disambiguation, Named Entity Recognition, Sentiment Analysis, Semantic Text Similarity, Language Identification, Text Summarisation, etc.
How do I start an NLP Project?
There are five steps you need to follow for starting an NLP project-. 1) Lexical analysis- It entails recognizing and analyzing word structures. The text is divided into paragraphs, phrases, and words using lexical analysis. 2) Syntactic analysis- It examines grammar, word layouts, and word relationships. 3) Semantic analysis retrieves all alternative meanings of a precise and semantically correct statement. 4) Discourse integration is governed by the sentences that come before it and the meaning of the ones that come after it. 5) Pragmatic analysis- It uses a set of rules that characterize cooperative dialogues to assist you in achieving the desired impact.
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AI Capstone Project with Deep Learning
About this Course
In this capstone, learners will apply their deep learning knowledge and expertise to a real world challenge. They will use a library of their choice to develop and test a deep learning model. They will load and pre-process data for a real problem, build the model and validate it. Learners will then present a project report to demonstrate the validity of their model and their proficiency in the field of Deep Learning.
Learning Outcomes: • determine what kind of deep learning method to use in which situation • know how to build a deep learning model to solve a real problem • master the process of creating a deep learning pipeline • apply knowledge of deep learning to improve models using real data • demonstrate ability to present and communicate outcomes of deep learning projects
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Syllabus - What you will learn from this course
Module 1 - loading data.
In this module, you will get introduced to the problem that we will try to solve throughout the course. You will also learn how to load the image dataset, manipulate images, and visualize them.
In this Module, you will mainly learn how to process image data and prepare it to build a classifier using pre-trained models.
In this Module, in the PyTorch part, you will learn how to build a linear classifier. In the Keras part, you will learn how to build an image classifier using the ResNet50 pre-trained model.
In this Module, in the PyTorch part, you will complete a peer review assessment where you will be asked to build an image classifier using the ResNet18 pre-trained model. In the Keras part, for the peer review assessment, you will be asked to build an image classifier using the VGG16 pre-trained model and compare its performance with the model that we built in the previous Module using the ResNet50 pre-trained model.
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May 26, 2021
20+ Deep Learning Projects with Python
24 deep learning projects solved and explained using python..
This article will take you through 20+ Deep Learning projects with Python programming language solved and explained for free.
Deep learning is a subset of Artificial Intelligence, which is an area that relies on learning and improving on its own by examining computer algorithms. While machine learning uses simpler concepts, these models work with artificial neural networks, designed to mimic the way humans think and learn.
Deep Learning Projects with Python
- Gender Detection
- End-to-end Machine Learning Project
- Predict Car Prices
- Image Recognition
- Image Classification
- Predict Fuel Efficiency
- Text Classification
- Real-Time Face Mask Detection
- Pneumonia Detection
- Face Mask Detection
- Number Plate Detection
- Flower Recognition
- Restaurant Recommendation System
- Earthquake Prediction Model
- Landmark Detection Model
- Chatbot with Deep Learning
- Title Generator
- Deepfake Detection
- Classify Nationalities
- Fashion Recommendation System
- Machine Translation Model
- Next Word Prediction Model
- Face Landmarks Detection
- Dog and Cat Classification
All of the above projects are solved and explained properly. I hope you liked this article on Deep Learning Projects with Python programming language. Feel free to ask your valuable questions in the comments section below.
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21 Machine Learning Projects – Datasets Included
Upgrading your machine learning, AI, and Data Science skills requires practice. To practice, you need to develop models with a large amount of data. Finding good datasets to work with can be challenging, so this article discusses more than 20 great datasets along with machine learning project ideas for you to tackle today.
By Shivashish Thakur , Digital Marketing, DataFlair .
To build a perfect model, you need a large amount of data. But finding the right dataset for your machine learning projects can be a challenging task. Luckily many organizations, researchers, and individuals have shared their machine learning projects and datasets, which we can use to build our own ML project ideas.
Scroll down to see 20+ machine learning and data science dataset and project ideas that you can use to practice and upgrade your skills today.
Machine Learning Projects:
1. enron email dataset.
The Enron Dataset is popular in natural language processing. It has more than 500K emails of over 150 users. The size of the data is around 432Mb. Out of 150 users, most of the users are the senior management of Enron.
Data Link : Enron email dataset
Machine Learning Project Idea : Using k-means clustering, you can build a model to detect fraudulent activities. K-means clustering is an unsupervised Machine learning algorithm. It separates the observations into k number of clusters based on the similar patterns in the data.
2. Chatbot Intents Dataset
The dataset for a chatbot is a JSON file that has disparate tags like goodbye, greetings, pharmacy_search, hospital_search, etc. Every tag has a list of patterns that a user can ask, and the chatbot will respond according to that pattern. The dataset is perfect for understanding how chatbot data works.
Data Link : Intents JSON Dataset
Machine Learning Project Idea : You can build a chatbot or understand the working of a chatbot by twisting and expanding the data with your observations. To build a Chatbot of your own, you need to have a good knowledge of Natural language processing concepts.
Source Code : Chatbot Project in Python
3. Flickr 30k Dataset
The Flickr 30k dataset has over 30,000 images, and each image is labeled with different captions. This dataset is used to build an image caption generator. And this dataset is an upgraded version of Flickr 8k used to build more accurate models.
Data Link : Flickr image dataset
Machine Learning Project Idea : You can build a CNN model that is great for analysing and extracting features from the image and generate a english sentence that describes the image that is called Caption.
4. Parkinson Dataset
Parkinson's is a disease that can cause a nervous system disorder and affects the movement. Parkinson dataset contains biomedical measurements, 195 records of people with 23 different attributes. This data is used to differentiate healthy people and people with Parkinson’s disease.
Data Link : Parkinson dataset
Machine Learning Project Idea : You can build a model that can be used to differentiate healthy people from people having Parkinson’s disease. The algorithm that is useful for this purpose is XGboost, which stands for extreme gradient boosting, and it is based on decision trees.
Source Code : ML Project on Detecting Parkinson’s Disease
5. Iris Dataset
The iris dataset is a beginner-friendly dataset that has information about the flower petal and sepal sizes. This dataset has 3 classes with 50 instances in every class, so only contains 150 rows with 4 columns.
Data Link : Iris dataset
Machine Learning Project Idea : Classification is the task of separating items into their corresponding class. You can implement a machine learning classification or regression model on the dataset.
6. ImageNet dataset
ImageNet is a large image database that is organized according to the wordnet hierarchy. It has over 100,000 phrases and an average of 1000 images per phrase. The size exceeds 150 GB. It is suitable for image recognition, face recognition, object detection, etc. It also hosts a challenging competition named ILSVRC for people to build more and more accurate models.
Data Link : Imagenet Dataset
Machine Learning Project Idea : To implement image classification on this huge database and recognize objects. CNN model (Convolutional neural networks) are necessary for this project to get accurate results.
7. Mall Customers Dataset
The Mall customers dataset holds the details about people visiting the mall. The dataset has an age, customer id, gender, annual income, and spending score. It gains insights from the data and divides the customers into different groups based on their behaviors.
Dataset Lin k: mall customers dataset
Machine Learning Project Idea : Segment the customers based on their gender, age, interest. It is useful in customized marketing. Customer segmentation is an important practice of dividing customers based on individual groups that are similar.
Source Code: Customer segmentation with Machine learning .
8. Google Trends Data Portal
Google trends data can be used to examine and analyze the data visually. You can also download the dataset into CSV files with a simple click. We can find out what’s trending and what people are searching for.
Data Link : Google trends datasets
9. The Boston Housing Dataset
This is a popular dataset used in pattern recognition. It contains information about the different houses in Boston based on crime rate, tax, number of rooms, etc. It has 506 rows and 14 different variables in columns. You can use this dataset to predict house prices.
Data Link: Boston dataset
Machine Learning Project Idea: Predict the housing prices of a new house using linear regression. Linear regression is used to predict values of unknown input when the data has some linear relationship between input and output variables.
10. Uber Pickups Dataset
The dataset has information about 4.5 million Uber pickups in New York City from April 2014 to September 2014 and 14 million more from January 2015 to June 2015. Users can perform data analysis and gather insights from the data.
Data Link : Uber pickups dataset
Machine Learning Project Idea : To analyze the data of the customer rides and visualize the data to find insights that can help improve business. Data analysis and visualization is an important part of data science. They are used to gather insights from the data, and with visualization, you can get quick information from the data.
11. Recommender Systems Dataset
This is a portal to a collection of rich datasets that were used in lab research projects at UCSD. It contains various datasets from popular websites like Goodreads book reviews, Amazon product reviews, bartending data, data from social media, etc that are used in building a recommender system.
Data Link : Recommender systems dataset
Machine Learning Project Idea : Build a product recommendation system like Amazon. A recommendation system can suggest your products, movies, etc. based on your interests and the things you like and have used earlier.
Source Code : Movie Recommendation System Project
12. UCI Spambase Dataset
Classifying emails as spam or non-spam is a very common and useful task. The dataset contains 4601 emails and 57 meta-information about the emails. You can build models to filter out the spam.
Data Link : UCI spambase dataset
Machine Learning Project Idea : You can build a model that can identify your emails as spam or non-spam.
13. GTSRB (German traffic sign recognition benchmark) Dataset
The GTSRB dataset contains around 50,000 images of traffic signs belonging to 43 different classes and contains information on the bounding box of each sign. The dataset is used for multiclass classification.
Data Link : GTSRB dataset
Machine Learning Project Idea : Build a model using a deep learning framework that classifies traffic signs and also recognizes the bounding box of signs. The traffic sign classification is also useful in autonomous vehicles for identifying signs and then taking appropriate actions.
Source Code : Traffic Signs Recognition Python Project
14. Cityscapes Dataset
This is an open-source dataset for Computer Vision projects. It contains high-quality pixel-level annotations of video sequences taken in 50 different city streets. The dataset is useful in semantic segmentation and training deep neural networks to understand the urban scene.
Data Link : Cityscapes dataset
Machine Learning Project Idea : To perform image segmentation and detect different objects from a video on the road. Image segmentation is the process of digitally partitioning an image into various different categories like cars, buses, people, trees, roads, etc.
15. Kinetics Dataset
There are three different datasets for Kinetics: Kinetics 400, Kinetics 600, and Kinetics 700 dataset. This is a large scale dataset that contains a URL link to around 6.5 million high-quality videos.
Data Link : Kinetics dataset
Machine Learning Project Idea : Build a human action recognition model and detect the action of a human. Human action recognition is recognized by a series of observations.
16. IMDB-Wiki dataset
The IMDB-Wiki dataset is one of the largest open-source datasets for face images with labeled gender and age. The images are collected from IMDB and Wikipedia. It has 5 million-plus labeled images.
Data Link : IMDB wiki dataset
Machine Learning Project Idea : Make a model that will detect faces and predict their gender and age. You can have categories in different ranges like 0-10, 10-20, 30-40, 50-60, etc.
17. Color Detection Dataset
The dataset contains a CSV file that has 865 color names with their corresponding RGB (red, green, and blue) values of the color. It also has the hexadecimal value of the color.
Data Link : Color Detection Dataset
Machine Learning Project Idea : The color dataset can use used to make a color detection app in which we can have an interface to pick a color from the image and the app will display the name of the color.
Source Code : Color Detection Python Project
18. Urban Sound 8K dataset
The urban sound dataset contains 8732 urban sounds from 10 classes like an air conditioner, dog bark, drilling, siren, street music, etc. The dataset is popular for urban sound classification problems.
Data Link : Urban Sound 8K dataset
Machine Learning Project Idea : We can build a sound classification system to detect the type of urban sound playing in the background. This will help you get started with audio data and understand how to work with unstructured data.
19. Librispeech Dataset
This dataset contains a large number of English speeches that are derived from the LibriVox project. It has 1000 hours of English-read speech in various accents. It is used for speech recognition projects.
Data Link : Librispeech dataset
Machine Learning Project Idea : Build a speech recognition model to detect what is being said and convert it into text. The objective of speech recognition is to automatically identify what is being said in the audio.
20. Breast Histopathology Images Dataset
This dataset contains 2,77,524 images of size 50×50 extracted from 162 mount slide images of breast cancer specimens scanned at 40x. There are 1,98,738 negative tests and 78,786 positive tests with IDC.
Data Link : Breast histopathology dataset
Machine Learning Project Idea : To build a model that can classify breast cancer. You build an image classification model with Convolutional neural networks.
Source Code : Breast Cancer Classification Python Project
21. Youtube 8M Dataset
The youtube 8M dataset is a large scale labeled video dataset that has 6.1 million Youtube video ids, 350,000 hours of video, 2.6 billion audio/visual features, 3862 classes, and 3 avg labels per video. It is used for video classification purposes.
Data Link : Youtube 8M
Machine Learning Project Idea : Video classification can be done by using the dataset, and the model can describe what video is about. A video takes a series of inputs to classify in which category the video belongs.
In this article, we saw more than 20 machine learning datasets that you can use to practice machine learning or data science. Creating a dataset on your own is expensive, so we can use other people’s datasets to get our work done. But we should read the documents of the dataset carefully because some datasets are free, while for some datasets, you have to give credit to the owner as stated by them.
Bio: Shivashish Thaku is an Analyst and technical content writer. He is a technology freak who loves to write about the latest cutting edge technologies that are transforming the world. He is also a sports fan who loves to play and watch football.
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Capstone Project Ideas : 150+ Topics
Have you ever wondered what the hardest part of framing a non-trivial capstone project is? With a long research path ahead of you, writing a capstone project is fraught with obstacles and pitfalls. However, the process becomes less complicated and more challenging as time passes.
The most challenging part of writing a capstone project is finding a topic that will help you articulate your thoughts in a disciplined way . On the contrary, selecting a tedious capstone topic can significantly impact your grades. Luckily, we have prepared a complete list of captivating project ideas to inspire your writing. Continue reading this blog, and you will see some outstanding topic ideas in psychology , information technology, nursing, marketing, and beyond.
Table of Contents
What is a Capstone Project?
Over the past few years, capstone projects have become a crucial part of the university degree curriculum. This whole project is similar to thesis writing but has a few differences. It is a project that an educational institute conducts to evaluate students’ understanding of their courses on different parameters. Students must write their capstone project by the end of their study programs.
Depending on your specific academic requirements, the context of your capstone project can significantly vary. It includes various structures, including multimedia presentation, film, execution, or paper. While the whole project seems complicated, in some ways, it can be rewarding as well. This project includes different scholarly exercises, including presenting their critical thinking, soft skills, teamwork abilities, communication, and viewpoints regarding their courses. This project helps young students research and analyze crucial data and how to present it proficiently.
Selecting an Engaging Topic for Your Capstone Project
Finding the perfect topic for your capstone project plays a significant role in framing the entire project. Choosing a tedious or monotonous topic can be a fatal mistake for students. With this, you can avoid drafting a monotonous capstone paper, no matter how well it may be written otherwise.
If you need help choosing good capstone topics, we have compiled a few practical suggestions to help you choose the right one.
- Brainstorm several ideas and explore the internet for interesting and engaging capstone project ideas.
- Remember that whatever topic you select will reflect the skills, knowledge, and insights you have gained throughout your semester. A good capstone topic will help you demonstrate those things more creatively and disciplined manner.
- The topic that you will select must be entirely manageable. Thus, consider choosing a specific case instead of a broad capstone topic.
- Make sure that the theme of your topic demonstrates REAL ongoing issues. Your goal should be to build solid arguments and provide genuine and reliable solutions for the mentioned problems.
- Conduct extensive research and check for previous studies on the same topic.
- Based on that research, narrow down the most unique and engaging topics. Choose the best out of all of them. If you need clarification on any topics, consult your professors and supervisors.
150+ Capstone Project Ideas
It is undoubtedly not easy to choose your “perfect” topic. The issue intensifies as every student in the class has to come up with their unique topics. You’re still on here to get some unique and intriguing capstone project ideas for your upcoming project. If so, then you are indeed on the right platform. Below are more than 150+ capstone project ideas that can help you choose an outstanding topic and start your research as early as possible.
Education Capstone Project Ideas
Check out the topics below to gain insight into some special education topics for capstone projects:
- Asthma education for nurses how can schools enhance the mental health of students?
- Determining the imperativeness of computers in education
- The importance of “game-based” learning for small kids
- Evaluating the impact of gender inequality in educational institutions
- The role of students’ motivation factors for scoring high grades in exams
- Be an obedient kid: are we teaching responsibility or obedience?
- Strategies for enhancing the performance of children
- Revisiting flexible learning in a digital age
- Education for children with special needs
- Anxiety attacks among students
- current changes in education models to promote a new way of learning
- Personal development and well-being in elementary schools
- Achieving a positive environment in schools through positive psychology
- Determining challenges and perspectives in the contemporary education system
- Social-emotional learning and developments
- Using intelligent board activities to boost engagement among students
- The student-teacher bond as an interpersonal relationship
Capstone Project Ideas for Nursing Students
Consider these suggestions for capstone project ideas for nursing and ensure the best result possible.
- The nursing shortage and its effect on health: a global problem
- Nurses’intervention to manage intensive care patients better
- nursing care management for asthma
- Patient-focused care
- Stress and burnout in nurse anaesthesia
- Managing and preventing dysfunctional behavioural symptoms in dementia patients
- Conceptual and Evidence-Based Practices for All Nurse Leaders
- Sexually transmitted diseases: implications and preventions
- The global need for extensive emergency care practices
- Point-of-care testing: an overview of the recent advances and trends
- Pain-management practices in healthcare sectors
- Analyzing the role of government in making efficient nursing practices
- The extensive role of the nursing profession in healthcare
- How do government rules and policies affect nursing as a profession?
- What strategies do the nurses follow to treat patients with disabilities?
- What was the role of the healthcare department in controlling the Corona virus?
- The significance of nurses in providing primary care
- Emerging violence towards healthcare departments by patients
Marketing Capstone Project Examples You Can Work On
The following are some captivating marketing ideas for your capstone projects:
- Understanding brand management and the best strategies to boost your brand
- The importance of visuals in your advertising campaigns
- impact of gender on customer purchasing behavior
- The globalization of marketing
- A deep analysis of the marketing strategies of Elon Musk
- Problems associated with e-commerce marketing
- Strategies and principles of international marketing
- Analyzing an effective marketing strategy
- Consumer buying behaviors
- The importance of social media for creating a strong marketing strategy
- B2B and B2C marketing strategies
- Marketing and globalization
- Social media as a marketing avenue
- Marketing strategy of Nike: the concept of footwear customization
- Recent trends in product loyalty
- Marketing strategies to enhance customer engagement
- the efficiency of blogs for optimizing organic traffic
- Strategic content strategy for businesses
- Factors influencing customer retention
- Brand value-building strategies
Computer Science Capstone Project Topics
Are you seeking the top capstone project ideas as a computer science student? So, sit back and unwind because we have compiled some exciting and educational computer science capstone project ideas to help you earn top marks .
- Classification of images
- Emerging threats to cyber security
- Artificial intelligence in healthcare and medicine
- Analyzing the process of image processing
- Internet banking security concerns
- SaaS Technologies of the Modern Time
- Current dynamics in online auction systems
- E-authentication systems
- Android battery-saver mode
- Evolving social media usage
- Digitization of education in the 21st century
- Software quality techniques and best practices
- Understanding security vulnerabilities in OS security
- Game Theory Using Genetic Algorithms
- Use of computer navigation in surgical procedures
- Understanding artificial intelligence as a modern approach
Engineering Capstone Project Ideas
The following is a list of some good capstone topics for engineering students. You can draw inspiration from these topics and use them as they are for your upcoming projects:
- Procedure for making a self-flying robot
- Making a robotic arm
- Animatronic hand
- 3D printers: innovations for education
- IoT-based smart energy meter using GSM
- Brilliant Greenhouse Facilities in Agricultural Engineering
- home automation system
- motorized chain mechanism
- Solar and intelligent energy systems
- Smart Traffic Lighting Control Systems for smart cities
- Building a Suspension Mountain Bike
- Design and implementation of sensor-guided robotics
- Geological Data Collection and Assessment Techniques
- Schedule control systems in construction
- Solar panels control technological systems.
- IoT-based intelligent automation of greenhouses
- Software-defined radio technology
- Off-grid refrigerators
- Car with remote control
- Pedal-powered water purifier
Management Capstone Project Ideas
Business management students use the following management capstone project ideas as inspiration for framing their capstone projects.
- Analyzing customer service in hotels
- Understanding the theories of project management: a complete guide
- The latest news operations management trends for business in 2022
- How does customer service affect sales?
- Joint innovation management across different industries
- Practical strategies to manage overqualified candidates
- Profile evaluation of the project manager
- The importance of technology for driving more sales
- Diversity management in the age of globalization
- Internal promotion vs external hiring
- Integrating business continuity and crisis management
- Free clinic evaluation processes
- Analyzing the principles of supply chain management
- Understanding business conflict management and strategies
- Best 101 Public Relations Techniques
- The art of crafting a systematic supply chain management
- Exploring the impact of globalization on intercultural communication
- How do small businesses respond to a crisis?
- The imperativeness of job satisfaction among both employees and employers
- The necessity of risk management in organizations
Best Ideas for MBA Capstone Project
Have a look at the following MBA capstone project ideas to get started with your capstone project:
- New app market research analysis
- Marketing segmentation, targeting, and positioning
- Exploring and understanding corporate design strategy
- Making a business plan for start-ups: a theoretical perspective
- Stakeholder management systems and environmental competitiveness
- Cost-effective business management practices
- Developing management strategies for businesses in developing countries
- Foundations of social media marketing: techniques and their impact
- Corporate downsizing: a detailed analysis of the survivors
- Managing diversity for organizational efficiency
- Dealing with inner conflicts in large-scale enterprises
- The effect of e-learning on professional certification
- Evaluating the barriers to total quality management
- Principles and practices involved in human resource management
- Cross-cultural management: a global perspective
- Evaluating business ethics principles: The health of leadership
- Flaws or drawbacks of standardized tests
- Business outsourcing and offshoring
Accounting Topics for Capstone Projects
Use one of these great accounting topics for capstone projects as your topic to get inspired and kick-start your capstone accounting project:
- The issues with business approaches and accounting systems
- Proprietorship accounting
- Payroll management systems
- Accounting for sales and income
- Accounting and tax evasion are critical systems.
- Earnings management
- Accounting software: an overview
- Fixed asset accounting systems
- Accounting software
- The top three global recessions
- Accounting Methods for a Proprietorship
- Analyzing the international accounting standards
- Accounting theories for income
- Different types of accounting systems are used in global organizations across the world.
- Accounting theories for leased
- Understanding the imperativeness of paying tax
- The influence of the recession on personal finance
- International and regional accounting standards
- Accounting information system
Good Capstone Topics for Psychology
Nowadays, with the evolving awareness of psychological aspects in our societies, people show more consideration regarding psychological elements. With this, you might find many capstone topics for your projects. Have a look at the below-given list of the top, enticing psychology topics for capstone projects:
- Cultural impact on the psychology of an individual
- Evaluating psychological theories of crime
- The effect of culture on individuals
- Military psychology
- The distinction between long-term and short-term memory
- The influence of the environment on hyperactive children
- The effect of violent media games on children
- Understanding the psychology of a terrorist
- The psychological impact of abortion on mothers
- Understanding the psychological aspects of suicidal behavior
- Social support and psychological factors among the LGBTQ community
- Gender and depression among men
- Gender and depression among women
- Assessing juvenile sexual offenders
- How to cope with depression
- The psychology behind ethical and unethical behaviours
- How does trauma or sexual assault affect kids?
The Bottom Line
This list of topics will assist you in framing an outstanding capstone project for your academic session. Remember that a topic will serve as the basis for your entire paper, which you will draft. Therefore, take this seriously and select an innovative and unique topic from the above list. Furthermore, before jumping straight to the writing business, narrow down a few feasible topics and research each one. This topic will help you with your entire project.
Still confused? Get in touch with Edumagnate.com for any assistance!
By Alex Brown
I'm an ambitious, seasoned, and versatile author. I am experienced in proposing, outlining, and writing engaging assignments. Developing contagious academic work is always my top priority. I have a keen eye for detail and diligence in producing exceptional academic writing work. I work hard daily to help students with their assignments and projects. Experimenting with creative writing styles while maintaining a solid and informative voice is what I enjoy the most.
Applied Deep Learning Capstone Project
In this capstone project, you'll use either Keras or PyTorch to develop, train, and test a Deep Learning model. Load and preprocess data for a real problem, build the model and then validate it.
Choose your session:
About this course.
Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!
In this capstone project, you'lluse a Deep Learning library ofyour choice to develop, train, and test a Deep Learning model.Loadand preprocess data for a real problem, build the model and then validate it.
Finally, you will present a project report to demonstrate the validity of yourmodel andyour proficiency in the field of deep learning.
At a glance
- Institution: IBM
- Subject: Data Analysis & Statistics
- Level: Advanced
- Completed all courses in the Deep Learning Professional Certification Program
- Language: English
- Video Transcript: English
- Professional Certificate in Deep Learning
What you'll learn
- Determine what kind of Deep Learning method to use in which situation
- Know how to build a Deep Learning model to solve a real problem
- Master the process of creating a Deep Learning pipeline
- Apply knowledge of Deep Learning to improve models using real data
- Demonstrate ability to present and communicate outcomes of Deep Learning projects
About the instructors
Who can take this course, ways to take this course, interested in this course for your business or team.
NYU Center for Data Science
Harnessing Data’s Potential for the World
Master’s in Data Science
- Industry Concentration
- Admission Requirements
- Capstone Project
- Summer Research Initiative
- Financial Aid
- Summer Initiative
CDS master’s students have a unique opportunity to solve real-world problems through the capstone course in the final year of their program. The capstone course is designed to apply knowledge into practice and to develop and improve critical skills such as problem-solving and collaboration skills.
Students are matched with research labs within the NYU community and with industry partners to investigate pressing issues, applying data science to the following areas:
- Probability and statistical analyses
- Natural language processing
- Big Data analysis and modeling
- Machine learning and computational statistics
- Coding and software engineering
- Visualization modeling
- Neural networks
- Signal processing
- High dimensional statistics
Capstone projects present students with the opportunity to work in their field of interest and gain exposure to applicable solutions. Project sponsors, NYU labs, and external partners, in turn receive the benefit of having a new perspective applied to their projects.
“Capstone is a unique opportunity for students to solve real world problems through projects carried out in collaboration with industry partners or research labs within the NYU community,” says capstone advisor and CDS Research Fellow Anastasios Noulas. “It is a vital experience for students ahead of their graduation and prior to entering the market, as it helps them improve their skills, especially in problem solving contexts that are atypical compared to standard courses offered in the curriculum. Cooperation within teams is another crucial skill built through the Capstone experience as projects are typically run across groups of 2 to 4 people.”
The Capstone Project offers the opportunity for organizations to propose a project that our graduate students will work on as part of their curriculum for one semester. Information on the course along with a questionnaire to propose a project, can be found on the Capstone Fall 2022 Project Submission Form . If you have any questions, please reach out to [email protected] .
Best Fall 2022 Capstone Posters
- Leveraging Computer Vision to Map Cell Tower Locations to Enhance School Connectivity
Student Authors: Lorena Piedras, Priya Dhond, and Alejandro Sáez | Mentors: Iyke Derek Maduako (UNICEF)
- Neural Re-Ranking for Personalized Home Search
Student Authors: Giacomo Bugli, Luigi Noto, Guilherme Albertini | Mentors: Shourabh Rawat, Niranjan Krishna, and Andreas Rubin-Schwarz
Sequence Modeling for Query Understanding & Conversational Search
Student Authors: Lucas Tao, Evelyn Wang, Jun Wang, Cecilia Wu | Mentors: Amir Rahmani, Arun Balagopalan, Shourabh Rawat, and Najoung Kim
- Solving challenging video games in human-like ways
Student Authors: Brian Pennisi, Jiawen Wu, Adeet Patel, and Sarvesh Patki | Mentors: Todd Gureckis (NYU)
Best Fall 2022 Student Voted Posters
- Deep Learning Framework for Segmentation of Medical Images
Student Authors: Luoyao Chen, Mei Chen, Jinqian Pan | Mentors: Jacopo Cirrone (NYU)
- Galaxy Dataset Distillation
Student Authors: Xu Han, Jason Wang, Chloe Zheng | Mentors: Julia Kempe (NYU)
Best Fall 2022 Runner-Up Posters
- Dementia Detection from FLAIR MRI via Deep Learning
Student Authors: Jiawen Fan, Aiqing Li | Mentors: Narges Razavian (NYU Langone)
- Ego4d NLQ: Egocentric Visual Learning of Representations and Episodic Memory
Student Authors: Dongdong Sun; Rui Chen; Ying Wang | Mentors: Mengye Ren (NYU)
- Learning User Representations from Zillow Search Sessions using Transformer Architectures
Student Authors: Xu Han, Jason Wang, Chloe Zheng | Mentors: Shourabh Rawat (Zillow Group)
- Methane Emission Quantification through Satellite Images
Student Authors: Alex Herron, Dhruv Saxena, Xiangyue Wang | Mentors: Robert Huppertz (orbio.earth)
Fall 2022 Capstone Project List
- Data Science for Clinical Decision-making Support in Radiation Therapy
- Using Voter File Data to Study Electoral Reform
- Creating an Epigenomic Map of the Heart
- Career Recommendation
- Calibrating for Class Weights
- Assigning Locations to Detected Stops using LSTM
- Impact of YMCA Facilities on the Local Neighborhoods of Bronx
- Powering SMS Product Recommendations with Deep Learning
- Evaluation and Performance Comparison of Two Models in Classifying Cosmological Simulation Parameters
- Crypto Anomaly Detection
- Sequence Modeling for Query Understanding & Conversational Search
- Multi-Modal Graph Inductive Learning with CLIP Embeddings
- Multimodal Contract Segmentation
- Extraction of Causal Narratives from News Articles
- Detecting Erroneous Geospatial Data
- Improving Speech Recognition Performance using Synthetic Data
- Multi-document Summarization for News Events
- Multi-task learning in orthogonal low dimensional parameter manifolds
- Let’s Go Shopping: An Investigation Into a New Bimodal E-Commerce Dataset
- Training AI to recognize objects of interest to the blind community
- Classify Classroom Activities using Ambient Sound
- Database and Dashboard for RII
- Bitcoin Price Prediction Using Machine Learning Models
- Context Driven Approach to Detecting Cross-Platform Coordinated Influence Campaigns
- Invalid Traffic Detection Model Deployment
- Recalled Experiences of Death: Using Transformers to Understand Experiences and Themes
- Context-Based Content Extraction & Summarization from News Articles
- Neural Learning to Rank for Personalized Home Search
- Improve Speech Recognition Performance Using Unpaired Audio and Text
- Data Normalization & Generalization to Population Metrics
- Automated Judicial Case Briefing
- Cyber Threat Detection for News Articles
- MLS Fan Segmentation
- Near Real-Time Estimation of Beef and Dairy Feedlot Greenhouse Gas Emissions
- Do Better Batters Face Higher or Lower Quality Pitches?
Best Fall 2021 Capstone Posters
- Question Answering on Long Context
Student Authors: Xinli Gu, Di He, Congyun Jin | Project Mentor: Jocelyn Beauchesne (Hyperscience)
Multimodal Self-Supervised Deep Learning with Chest X-Rays and EHR Data
Student Authors: Adhham Zaatri, Emily Mui, Yechan Lew | Project Mentor: Sumit Chopra (NYU Langone)
Head and Neck CT Segmentation Using Deep Learning
Student Authors: Pengyun Ding, Tianyu Zhang | Project Mentor: Ye Yuan (NYU Langone)
- 3D Astrophysical Simulation with Transformer
Student Authors: Elliot Dang, Tong Li, Zheyuan Hu | Project Mentor: Shirley Ho (Flatiron Institute)
Multimodal Representations for Document Understanding (Best Student Voted Poster)
Student Authors: Pavel Gladkevich, David Trakhtenberg, Ted Xie, Duey Xu | Project Mentor: Shourabh Rawat (Zillow Group)
2021 Capstone Project List
- Accelerated Learning in the Context of Language Acquisition
- Analysis of Cardiac Signals on Patients with Atrial Fibrillation
- Applications of Neural Radiance Fields in Astronomy
- Automatic Detection of Alzheimer’s Disease with Multi-Modal Fusion of Clinical MRI Scans
- Automatic Transcription of Speech on SAYCam
- Automatic Volumetric Segmentation of Brain Tumor Using Deep Learning for Radiation Oncology
- Automatically Identify Applicants Who Require Physician’s Reports
- Building a Question-Answer Generation Pipeline for The New York Times
- Coupled Energy-Based Models and Normalizing Flows for Unsupervised Learning
- Data Classification Processing for Clinical Decision-making Support in Radiation Therapy
- Deep Active Learning for Protest Detection
- Estimating Intracranial Pressure Using OCT Scans of the Eyeball
- Graph Neural Networks for Electronic Health Record (EHR) Data
- Head and Neck CT Image Segmentation
- Head Movement Measurement During Structural MRI
- Image Segmentation for Vestibular Schwannoma
- Investigation into the Functionality of Key, Query, Value Sub-modules of a Transformer
- Know Your Worth: An Analysis of Job Salaries
- Machine learning-based computational phenotyping of electronic health records
- Modeling the Speed Accuracy Tradeoff in Decision-Making
- Multi-modal Breast Cancer Detection
- Multi-Modal Deep Learning with Medical Images and EHR Data
- Multimodal Representations for Document Understanding
- Nematode Counting
- News Clustering and Summarization
- Post-surgical resection mapping in epilepsy using CNNs
- Predicting Grandstanding in the Supreme Court through Speech
- Predicting Probability of Post-Colectomy Hospital Readmission
- Prediction of Total Knee Replacement Using Radiographs and Clinical Risk Factors
- Reinforcement Learning for Option Hedging
- Representation Learning Regarding RNA-RBP Binding
- Self-Supervised Learning of Medical Image Representations Using Radiology Reports
- The Study of American Public Policy with NLP
- Topical Aggregation and Timeline Extraction on the NYT Corpus
- Unsupervised Deep Denoiser for Electron-Microscope Data
- Using Deep Learning and FBSDEs to Solve Option Pricing and Trading Problems
- Vision Language Models for Real Estate Images and Descriptions
Featured 2020 Capstone Projects
Speak or Chat with Me: End-to-End Spoken Language Understanding System with Flexible Inputs
By Sujeong Cha, Wangrui Hou, Hyun Jung, My Phung, Michael Picheny, Hong-Kwang Kuo, Samuel Thomas, Edmilson MoraisJain
Accented Speech Recognition Inspired by Human Perception
By Xiangyun Chu, Elizabeth Combs, Amber Wang, Michael Picheny
Diarization of Legal Proceedings. Identifying and Transcribing Judicial Speech from Recorded Court Audio
By Jeffrey Tumminia, Amanda Kuznecov, Sophia Tsilerides, Ilana Weinstein, Brian McFee, Michael Picheny, Aaron R. Kaufman
2020 Capstone Project List
- 2D to 3D Video Generation for Surgery (Best Capstone Poster)
- Action Primitive Recognition with Sequence to Sequence Models towards Stroke Rehabilitation
- Applying Self-learning Methods on Histopathology Whole Slide Images
- Applying Transformers Models to Scanned Documents: An Application in Industry
- Beyond Bert-based Financial Sentimental Classification: Label Noise and Company Information
- Bias and Stability in Hiring Algorithms (Best Capstone Poster)
- Breast Cancer Detection using Self-supervised Learning Method
- Catastrophic Forgetting: An Extension of Current Approaches (Best Capstone Poster)
- ClinicalLongformer: Public Available Transformers Language Models for Long Clinical Sequences
- Complication Prediction of Bariatric Surgery
- Constraining Search Space for Hardware Configurations
- D4J: Data for Justice to Advance Transparency and Fairness
- Data-driven Diesel Insights
- Deep Learning to Study Pathophysiology in Dermatomyositis
- Detection Of Drug-Target Interactions Using BioNLP
- Determining RNA Alternative Splicing Patterns
- Developing a Data Ecosystem for Refugee Integration Insights
- Diarizing Legal Proceedings
- Estimating the Impact of the Home Health Value-Based Purchasing Model
- Extracting economic sentiment from mainstream media articles
- Food Trend Detection in Chinese Financial Market
- Forecasting Biodiesel Auction Prices
- Generative Adversarial Networks for Electron Microscope Image Denoising
- Graph Embedding for Question Answering over Knowledge Graphs
- Impact of NYU Wasserman Resources on Students’ Career Outcomes
- Improving Accented Speech Recognition Through Multi-Accent Pre-Exposure
- Improving Synthetic Image Generation for Better Object Detection
- Learning-based Model for Super-resolution in Microscopy Imaging
- Modeling Human Reading by a Grapheme-to-Phoneme Neural Network
- Movement Classification of Macaque Neural Activity
- New OXXO Store in Brazil and Revenue Prediction
- Numerical Relativity Interpolations using Deep Learning
- One Medical Passport: Predictive Obstructive Sleep Apnea Analysis
- Online Student Pathways at New York University
- Predicting YouTube Trending Video Project
- Promotional Forecasting Model for Profit Optimization
- Question Answering on Tabular Data with NLP
- Raizen Fuel Demand Forecasting
- Reach for the stars: detecting astronomical transients
- Reverse Engineering the MOS 6502 Microprocessor
- Selecting Optimal Training Sets
- Synthesizing baseball data with event prediction pretraining
- Train ETA Estimation for Rumo S.A.
- Training a Generalizable End-to-End Speech-to-Intent Model
- Utilizing Machine Learning for Career Advancement and Professional Growth
Best Fall 2019 Capstone Projects
- Inferring the Topic(s) of Wikipedia Articles
By Marina Zavalina, Sarthak Agarwal, Chinmay Singhal, Peeyush Jain
Option Portfolio Replication and Hedging in Deep Reinforcement Learning
By Bofei Zhang, Jiayi Du, Yixuan Wang, Muyang Jin
Adversarial Attacks Against Linear and Deep-Learning Regressions in Astronomy
By Teresa Huang, Zacharie Martin, Greg Scanlon, Eva Wang Mentors: Soledad Villar, David W. Hogg
2019 Capstone Project List
- Adversarial Attacks Against Linear and Deep-learning Regressions in Astronomy
- Automated Breast Cancer Screening
- Automatic Legal Case Summaries
- Cross-task Transfer Between Language Understanding Tasks in NLP
- Dark Matter and Stellar Stream Detection using Deep Learned Clustering
- Exploiting Google Street View to Generate Global-scale Data Sets for Training Next Generation Cyber-Physical Systems
- Federated Incremental Learning
- Fraud Detection in Monetary Transactions Between Bank Accounts
- Guided Image Upsampling
- Improving State of the Art Cross-Lingual Word-Embeddings
- Latent Semantic Topics Distribution Over Web Content Corpus
- Lease Renewal Probability Prediction
- Machine Learning for Adaptive Fuzzy String Matching
- Market Segmentation from Retailer Behavior
- Modeling the Experienced Dental Curriculum from Student Data
- Modelling NBA Games
- Movie Preference Prediction
- MRI Image Reconstruction
- NLP Metalearning
- Predict next sales office location
Predicting Stock Market Movements using Public Sentiment Data & Sequential Deep Learning Models
- Predictive Maintenance Techniques
- Reinforcement Learning for Replication and Hedging of Option
- Self-supervised Machine Listening
Sentence Classification of TripAdvisor ‘Points-of-Interest’ Reviews
- Simulating the Dark Matter Distribution of the Universe with Deep Learning
- SMaPP2: Joint Embedding of User-content and Network Structure to Enable a Common coordinate that captures ideology, geography and user topic spectrum.”
- Sparse Deconvolution Methods for Microscopy Imaging Data Analysis
- Stereotype and Unconscious Bias in Large Datasets
- Structuring Exploring and Exploiting NIH’s Clinical Trials Database
- The Analysis, Visualization, and Understanding of Big Urban Noise Data
- Unsupervised and Self-supervised Learning for Medical Notes
- Unsupervised Generative Video Dubbing
- Using Deep Generative Models to de-noise Noisy Astronomical Data
Featured Academic Capstone Projects
Deep Learning for Breast Cancer Detection
By Jason Phang, Jungkyu (JP) Park, Thibault Fevry, Zhe Huang, The B-Team
Brain Segmentation Using Deep Learning
By Team 22/7 | Chaitra V. Hegde | Advisor: Narges Razavian
Predict Total Knee Replacement Using MRI With Supervised and Semi-Supervised Networks
By Team Glosy: Hong Gao, Mingsi Long, Yulin Shen, and Jie Yang
Featured Industry Capstone Projects
Determining where New York Life Insurance should open its next sales office
NBA Shot Prediction with Spatio-Temporal Analysis
Other past capstone projects.
- Active Physical Inference via Reinforcement Learning
- Deep Multi-Modal Content-User Embeddings for Music Recommendation
- Fluorescent Microscopy Image Restoration
- Learning Visual Embeddings for Reinforcement Learning
- Offensive Speech Detection on Twitter
- Predicting Movement Primitives in Stroke Patients using IMU Sensors
- Recurrent Policy Gradients For Smooth Continuous Control
- The Quality-Quantity Tradeoff in Deep Learning
- Trend Modeling in Childhood Obesity Prediction
- Twitter Food/Activity Monitor
Towards Data Science
Jun 5, 2021
6 Best Projects For Image Processing With Useful Resources
The six best projects to work with image processing and machine learning with useful links and technical resources.
We are surrounded by beautiful visuals and colorful images all around us. Looking at the natural environment and capturing pictures results in a fun time. While our eyes can visualize the colors and various notions of an image with ease, it is a complex process for computers to recognize these same images. For the analysis of these image visuals, we make use of image processing algorithms with either machine learning or deep learning to create fabulous projects.
Computer Vision is one of the most intriguing aspects of study in the modern world. The tasks which were once perceived to be almost impossible for more mere machines to perform and compute are achieved with relative ease using the latest CV algorithms and techniques. With the rise of all the elements of computer vision in the modern computation era, we can create some high-quality projects.
From the numerous options and various choices of image visuals surrounding us, we can create some top-notch projects from scratch. In this article, our objective is to list six of the best image processing projects that you can achieve with the help of computer vision, machine learning, or neural networks if required.
All the projects listed in this article are my personal six picks for any enthusiast of computer vision. Ensure that you refer to the useful links, resources, and citations that are stated in this article for further guidance. They will walk you through most of the problems that you might encounter while tackling these projects.
1. Getting Started with PIL and OpenCV
Firstly, it is significant to understand how images work in the natural world and how they are perceived by computers to process and analyze these digital visuals. All images are interpreted in the format of 0’s and a range until 255’s. The format of colored images is in the form of RGB, where a value is interpreted in a three-dimensional array. Similarly, for grayscale images, we only have two spectrums consisting of white and black counterparts.
The Python Imaging Library (PIL) is one of the main methods to add image processing capabilities to your Python interpreter. Thanks to this library which provides extensive file format support, you can perform most tasks efficiently. It has an effective internal representation and fairly powerful image processing capabilities. The overall core image library is designed for the purpose of having faster access to data elements stored in a few basic pixel formats. Hence, this library is a great starting point because it provides a solid foundation for the users with an accessible, general image processing tool (check documentation link provided below for more information).
Below is a simple code block to understand some of the basic features of the PIL library.
For further experimentation and understanding of the pillow library, I would recommend checking out the official documentation and experimenting with more images and modules available to you with this tool.
The next library to learn to create wonderful projects is with the help of the open-cv computer vision library. Once you are familiar with the pillow library, you can start experimenting with your knowledge of these images with the help of the cv2 library. With the help of this tool, you can manipulate images, performing resizing by changing their dimensions, convert their colors from one format to another, and so much more. It is worth exploring from scratch and gaining the most knowledge that you can out of this library.
If you are interested in learning most of the essential aspects of computer vision from scratch, along with all the respective codes to solve some complex tasks, I would recommend all of you check out the article provided below. It covers most of the essentials required for beginners to get started with computer vision and eventually master it.
OpenCV: Complete Beginners Guide To Master the Basics Of Computer Vision With Code!
A tutorial with codes to master all the important concepts of computer vision and how to implement them using opencv.
2. Image Based Attendance System
The traditional method of raising your hand in a classroom to say “present ma’am” or “yes ma’am” or whatever other things you would say is kind of fading away. With the introduction of online classes where students and teachers interact through an online platform, it would be harder to take attendance in the more traditional way. However, computer vision comes to the rescue to help us create an image-based attendance system for taking attendance online with the help of your pixelated pictures!
Let us discuss some methodologies in which you could potentially approach this problem. One classic method is to ensure that you have a few images of all the respective students and classmates. If you cannot encompass a larger dataset, you can use methods of data augmentation to increase the amount of data that you have stored. Once you are able to collect a decent number of datasets for this particular task, you can process these images and build a deep learning model for achieving top-notch results.
If you are interested in exploring the theoretical aspects related to the task of the Image-Based Attendance System, then the Research paper should be a fantastic starting point for you to explore more theoretical knowledge and understanding of the concept. However, if you are more so interested in the practical coding implementation of the procedure, then this article guide should help you as a reference for implementing your own solutions as well.
3. Face Mask Detection
During the time of this pandemic, there are some strict regulations that need to be followed to maintain the decorum of the city, state, or country. Since we can’t always have the official authority on the lookout for some people not abiding by the rules, we can construct a face mask detection project that will enable us to figure out if a particular person is wearing a mask or not. During this time, with strict regulations of the lockdown, it would be a brilliant idea to implement this project to contribute to the upkeeping of the society.
Hence, a project in which you can process images of an entire area or region by tracking people on the road or streets to analyze if they are wearing masks or not would be a spectacular idea. With the help of image processing algorithms and deep learning techniques, you can compute images of people who are wearing masks. The following Kaggle dataset for face mask detection would be a great starting point to analyze the training images for achieving an overall high accuracy.
One of the best ways to approach this problem would be to make use of transfer learning models such as VGG-16, face-net, RESNET-50, and other similar architectures to see what method helps you to achieve the best results. As a starting point, I would highly recommend checking out one of my previous articles on smart face lock systems, where we construct some high-level face recognition systems. You can use a similar method for faces with no mask and faces with a mask to solve this type of task.
Smart Face Lock System
Building a high accuracy face recognition model, 4. number plate recognition.
One of the best projects to work with alphanumeric character identification is with the help of number plate images. There are several methods that we can employ to solve the problems that have letters, digits, and numbers embedded in images. We can use deep learning techniques, Optical character recognition (OCR) technologies, a combination of image processing and natural language processing (NLP), computer vision methods, and so much more.
The vast methodologies in which you can approach this problem provide you with the opportunity to explore all these methods by yourself with the models you develop. Finding out what technique will help you achieve the best results is rather intriguing. With a deep learning approach, you can collect the required datasets and information from Kaggle for the Vehicle Number Plate Detection . Once you collect enough information, you can build your own custom models or use transfer learning models to see what gives you the desired results.
If you want to use a more unique approach to solve problems, It is recommended that you check out one of my previous articles on optical character recognition (OCR). Using the OCR technology, you can interpret most of the data present in an image with relative ease. The OCR engine tries to analyze the characters in the image and find the appropriate solutions. To learn more about this topic in detail, check out the link provided below. You can also try out other unique methods to see which technique yields the best results.
Getting Started with Optical Character Recognition using Python
An intuitive understanding and brief introduction to optical character recognition from scratch, 5. medical image segmentations.
One of the most significant contributions of image processing, computer vision, machine learning, and deep learning is in the medical field. They contribute to analyzing and visualizing many of the highly complex abnormalities that could occur in human beings. Tasks such as diabetic retinopathy, cancer detections, x-ray analysis, and other crucial medical processing tasks require the use of deep learning models with image processing for highly accurate results.
While most projects require high accuracy of prediction, this statement becomes much more critical in the tasks of image segmentation in the medical field. From the time of biomedical image segmentation in 2015 with the U-Net architecture, there have been more variations of this architecture as well as many different types of models that are continuously being constructed for obtaining the best possible results in every scenario.
One of the best places to receive images and video files for any task related to medical image segmentation can be obtained from the DICOM library . By accessing this link, you will be directed to a section where you can download medical images and videos for performing scientific computations.
You can also utilize the Diabetic Retinopathy dataset from Kaggle to get started with a popular challenge on computing the image segmentation of the eyes as well as detecting if a person suffers from a condition of eyes. Apart from the tasks mentioned above, there are tons of biomedical image processing and tasks that are available at your disposal. Feel free to test them out and experiment with them.
6. Emotion and Gesture Recognition
Looking at the above image, one might wonder what that particular hand sign could be classified as. There are several gestures that people throw out as a form of communication. With the help of the appropriate images, one can figure out the best methods of classifying the gestures accordingly. Similarly, you might want to figure out the emotions on one face. Whether the person shows signs of happiness, sadness, anger, or any other similar emotion, you can build an AI model that will perform the following classification.
Emotions and gestures are integral parts of human activities. Albeit a bit harder in comparison to some of the other projects mentioned in this article, we can construct a computer vision and deep learning model to perform the following task. To approach this problem, you can make use of the facial emotions recognition ( Kaggle’s fer2013 dataset ) for emotions classification and the American sign language ( ASL Alphabet dataset ) for performing the computation of gestures.
Once we have all the required datasets, you can construct your deep learning architectures with the help of computer vision for the implementation of these projects. With the combination of neural networks and image processing, you can start working on both emotions and gesture detection to get high-quality results with decent losses and accuracy.
The links provided below are two of the best guides in which you can perform the activity of human emotion and gesture recognition from scratch. I have covered almost every single aspect required for the perfect computation of these tasks, including the pre-processing of datasets, visualization of the data, and the construction of the architecture from scratch. Feel free to refer to them to obtain the best possible information on performing these tasks.
Human Emotion and Gesture Detector Using Deep Learning: Part-1
Understanding how to build a human emotion and gesture detector with deep learning from scratch., human emotion and gesture detector using deep learning: part-2, diving deeper into human emotion and gesture recognition, conclusion:.
More than 500 million years ago, vision became the primary driving force of evolution’s ‘big bang’, the Cambrian Explosion, which resulted in explosive speciation of the animal kingdom. 500 million years later, AI technology is at the verge of changing the landscape of how humans live, work, communicate, and shape our environment. — Fei-Fei Li
Using artificial intelligence and computer vision to work with images, pictures, and any other type of visuals is currently of tremendous significance across multiple fields. Apart from the six projects mentioned in this article, there are millions of more project ideas that you can implement on your own from scratch, enabling you to become more proficient with image processing and computer vision tasks.
For any tasks related to image processing, it becomes essential for one to understand how images work in the core. With this prior knowledge and understanding of basic concepts, you can easily implement more complex image processing algorithms, machine learning methodologies, and deep learning techniques with greater ease.
With the increasing demand for image processing and computer vision projects in modern applications, it is the best time for anyone who is an enthusiast of the following fields to invest their valuable efforts and resources. Not only can you decode the characteristics and working principles of images, but you can also employ your skills in high complexity tasks such as self-driving cars for the overall betterment and improvement in the lifestyle of people.
If you have any queries related to the various points stated in this article, then feel free to let me know in the comments below. I will try to get back to you with a response as soon as possible.
Check out some of my other articles that you might enjoy reading!
Best PC Builds For Deep Learning In Every Budget Ranges
7 best free tools for data science and machine learning, best library to simplify math for machine learning, using the sympy python library to simplify differential and integral calculus for machine learning, 6 best programming practices, the six programming tips that every programmer and data scientist should follow, 5 essential skills to develop as a data scientist, exploring the five significant soft skills that every successful data scientist must have.
Thank you all for sticking on till the end. I hope all of you enjoyed reading the article. Wish you all a wonderful day!
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Love to explore and learn new concepts. Extremely interested in AI, deep learning, robots, and the universe. LinkedIn — www.linkedin.com/in/bharath-k-421090194
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Deep Learning Project Idea - To start with deep learning, the very basic project that you can build is to predict the next digit in a sequence. Create a sequence like a list of odd numbers and then build a model and train it to predict the next digit in the sequence. A simple neural network with 2 layers would be sufficient to build the model. 3.
This is by far the most common: Pick an application that interests you, and explore how best to apply learning algorithms to solve it. Algorithmic project. Pick a problem or family of problems, and develop a new learning algorithm, or a novel variant of an existing algorithm, to solve it. Theoretical project.
Deep Learning Project Ideas: Intermediate Level 4. Digit Recognition System As the name suggests, this project involves developing a digit recognition system that can classify digits based on the set tenets. Here, you'll be using the MNIST dataset containing images (28 X 28 size).
Deep Learning Projects For Beginners 1. Image Classification Using CIFAR-10 Dataset 2. Dog's Breed Identification 3. Human Face Detection 4. Music Genre Classification System Intermediate Deep Learning Projects 5. Drowsy Driver Detection System 6. Breast Cancer Detection Ssing Deep Learning 7. Gender Recognition Using Voice 8. Chatbot 9.
Segmenting remote sensing images. The deep learning projects for final year students are intended to address the student's doubts and opportunities to utilize what they've learned during the course. Final-year projects could very well focus on the creation of a software package as well as scientific research.
Capstone and senior design project ideas for undergraduate and graduate students to gain practical experience and insight into technology trends and industry directions.
To create a text summarization system with machine learning, you'll need familiarity with Pandas, Numpy, and NTLK. You'll also need to use unsupervised learning algorithms like the Glove method (developed by Stanford) for word representation. Find a step-by-step guide to text summarization system building here.
Before selecting the deep learning capstone project ideas, first, know the various kinds of deep learning networks. Since one type is different from other deep learning networks due to its functions and features. Also, it has different targets to achieve in deep learning-related projects.
Capstone Project Ideas for Management Course Studying management means dealing with the most varied spheres of life, problem-solving in different business areas, and evaluating risks. The challenge starts when you select the appropriate topic for your capstone project. Let the following list help you come up with your ideas.
This capstone project is a huge deal in the social media market. Right from platforms like Instagram, that is currently gushing with every human's opinions and emotions, sentiment analysis plays a very significant role to dealers, marketers, influencers of brands and other marketing ways.
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In this capstone, learners will apply their deep learning knowledge and expertise to a real world challenge. They will use a library of their choice to develop and test a deep learning model. They will load and pre-process data for a real problem, build the model and validate it. Learners will then present a project report to demonstrate the ...
Here is an awesome Github repo containing some cool project ideas using Deep learning. Check it out Here. Also, do check out my recent posts: Beginners Guide to XGBoost !!!!! comment 11 Comments. Hotness. arrow_drop_down. Shivam. Posted 3 years ago. arrow_drop_up 1. more_vert. format_quote. Quote. link.
Pipeline: A Data Engineering Resource. 3 Data Science Projects That Got Me 12 Interviews. And 1 That Got Me in Trouble. Zach Quinn. in. Pipeline: A Data Engineering Resource.
Machine Learning Project Idea: You can build a CNN model that is great for analysing and extracting features from the image and generate a english sentence that describes the image that is called Caption. 4. Parkinson Dataset. Parkinson's is a disease that can cause a nervous system disorder and affects the movement.
Engineering Capstone Project Ideas The following is a list of some good capstone topics for engineering students. You can draw inspiration from these topics and use them as they are for your upcoming projects: Procedure for making a self-flying robot Making a robotic arm Animatronic hand 3D printers: innovations for education
Enroll to learn more, complete the course and claim your badge! In this capstone project, you'lluse a Deep Learning library ofyour choice to develop, train, and test a Deep Learning model.Loadand preprocess data for a real problem, build the model and then validate it. Finally, you will present a project report to demonstrate the validity of ...
The capstone course is designed to apply knowledge into practice and to develop and improve critical skills such as problem-solving and collaboration skills. Students are matched with research labs within the NYU community and with industry partners to investigate pressing issues, applying data science to the following areas: Capstone projects ...
4. Number Plate Recognition. Photo by Thomas Millot on Unsplash. One of the best projects to work with alphanumeric character identification is with the help of number plate images. There are several methods that we can employ to solve the problems that have letters, digits, and numbers embedded in images.
Deep Learning Bootcamp with 5 Capstone Projects | Udemy Deep Learning Bootcamp with 5 Capstone Projects 4.3 (171 ratings) 19,131 students $14.99 $19.99 Development Data Science Deep Learning Preview this course Deep Learning Bootcamp with 5 Capstone Projects Learn about Deep Learning - ANN, CNN, RNN, LSTMs along with Real Time Capstone Projects