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• Data, Analysis, & AI

13 Best Machine Learning Books in 2024 | Beginner to Pro

In this article, we share the 13 best machine learning books in 2024. Whether you’d like to land a job as a machine learning engineer or want to further your data science career by learning new skills, we’ve included up-to-date machine learning books for beginners and experienced professionals.

In 2024 and onwards, machine learning continues to be an essential tool for businesses that want to unlock value from data. Plus, when you factor in that the Bureau of Labor Statistics reports a median salary of more than $100,000 for data scientists, it’s fair to say that reading the best machine learning books can be highly rewarding!

You may be asking, how can I develop machine learning skills? Well, alongside taking some of the best machine learning courses , you cannot go wrong by reading one of the best machine learning books.

So if you’re ready, let’s dive into the best machine learning books in 2024 to help you develop the skills you need to excel in this field.

Featured Machine Learning Books [Editor’s Picks]

• Machine Learning for Absolute Beginners: A Plain English Introduction
• Introduction to Machine Learning with Python: A Guide for Data Scientists
• AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence
• Machine Learning Books for Beginners

1. The Hundred-Page Machine Learning Book

Check Price

Author(s) – Andriy Burkov

Pages – 160

Latest Edition – First Edition

Publisher – Andriy Burkov

Format – Kindle/Hardcover/Paperback

Why we chose this book

Is it possible to learn machine learning in only 100 pages? This beginner's book for Machine Learning uses an easy-to-comprehend approach to help you learn how to build complex AI systems, pass ML interviews, and more.

This is an ideal book if you want a concise guide for machine learning that succinctly covers key concepts like supervised & unsupervised learning, deep learning, overfitting, and even essential math topics like linear algebra, probably, and stats.

• Fundamental ML concepts, including evaluation & overfitting
• Supervised learning via linear regression, logistic regression, & random forests
• Unsupervised Learning via clustering & dimensionality reduction
• Deep Learning via neural networks (NN)
• Essential math topics like linear algebra, optimization, probability and statistics

2. Machine Learning for Absolute Beginners: A Plain English Introduction

Author(s) – Oliver Theobald

Pages – 179

Latest Edition – Third Edition

Publisher – Scatterplot Press

Format – Kindle/Paperback/Hardcover

If you’re interested in learning machine learning but have no prior experience, this book is ideal for you, as it doesn’t assume prior knowledge, coding skills, or math.

With this book, you’ll learn the basic concepts and definitions of ML, types of machine learning models (supervised, unsupervised, deep learning), data analysis and preprocessing, and how to implement these with popular machine learning libraries like scikit-learn, NumPy, Pandas, Matplotlib, Seaborn, and TensorFlow.

• Intro to Python programming language and to use with machine learning
• Basics of deep learning and Neural Networks (NN)
• Covers clustering and supervised/unsupervised algorithms
• Python ML Libraries, including scikit-learn, NumPy, Pandas, and Tensorflow
• The theory behind feature engineering and how to approach it

3. Machine Learning for Dummies

Author(s) – John Paul Mueller and Luca Massaron

Pages – 464

Latest Edition – Second Edition

Publisher – For Dummies

Format – Kindle/Paperback

This book aims to make the reader familiar with the basic concepts and theories of machine learning in an easy way (hence the name!). It also focuses on practical and real-world applications of machine learning.

This book will teach you underlying math principles and algorithms to help you build practical machine learning models. You’ll also learn the history of AI and ML and work with Python, R, and TensorFlow to build and test your own models. You’ll also use up-to-date datasets and learn best practices by example.

• Tools and techniques for cleaning, exploring, and preprocessing data
• Unsupervised, supervised, and deep learning methods
• Evaluating model performance with accuracy, precision, recall, and F1 score
• Best practices and tips for feature selection, model selection, and avoiding overfitting

4. Introduction to Machine Learning with Python: A Guide for Data Scientists

Author(s) – Andreas C. Müller & Sarah Guido

Pages – 392

Publisher – O’Reilly Media

This book is a practical guide for beginners to learn how to create machine learning solutions as it focuses on the practical aspects of machine learning algorithms with Python and scikit-learn.

The authors don’t focus on the math behind algorithms but rather on their applications and fundamental concepts. It also covers popular machine learning algorithms, data representation, and more, making this a great resource for anyone looking to improve their machine learning and data science skills.

• Covers the basic concepts and definitions of machine learning
• Addresses supervised, unsupervised, and deep learning models
• Includes techniques for representing data 
• Includes text processing techniques and natural language processing

5. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Author(s) – Aurélien Géron

Pages – 861

This book is ideal for learning the popular machine learning libraries, Keras, Scikit-Learn, and TensorFlow.

Being an intermediate-level book, you’ll need Python coding experience, but you’ll then be able to complete a range of well-designed exercises to practice and apply the skills you learn.

• How to construct and train deep neural networks
• Covers deep reinforcement learning
• Learn to use linear regression and logistic regression

6. Understanding Machine Learning

Author(s) – Shai Shalev-Shwartz and Shai Ben-David

Pages – 410

Publisher – Cambridge University Press

Format – Hardcover/Kindle/Paperback

This book offers a structured introduction to machine learning by diving into the fundamental theories, algorithmic paradigms, and mathematical derivations of machine learning.

It also covers a range of machine learning topics in a clear and easy-to-understand manner, making it good for anyone from computer science students to others from fields like engineering, math, and statistics.

• Covers the computational complexity of various ML algorithms
• Covers convexity and stability of ML algorithms
• Learn to construct and train neural networks

7. AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence

Author(s) – Laurence Moroney

Pages – 390

This machine learning book is aimed at programmers who want to learn about artificial intelligence (AI) and ML concepts like supervised and unsupervised learning, deep learning, neural networks, and practical implementations of ML techniques with Python and TensorFlow.

This book also covers the theoretical and practical aspects of AI and ML, along with the latest trends in the field. Overall, it’s a comprehensive resource for programmers who want to implement ML in their own projects.

• Covers how to build models with TensorFlow
• Learn about supervised and unsupervised learning, deep learning, and neural networks
• Covers best practices for running models in the cloud

8. Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

Author(s) – Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili

Pages – 774

Publisher – Packt Publishing

This PyTorch book is a comprehensive guide to machine learning and deep learning, providing both tutorial and reference materials. It dives into essential techniques with detailed explanations, illustrations, and examples, including concepts like graph neural networks and large-scale transformers for NLP.

This book is mostly aimed at developers and data scientists who have a solid understanding of Python but want to learn about machine learning and deep learning with Scikit-learn and PyTorch.

• Learn PyTorch and scikit-learn for machine learning and deep learning
• Covers how to train machine learning classifiers on different data types
• Best practices for preprocessing and cleaning data
• Advanced Machine Learning Books

9. The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Author(s) – Trevor Hastie, Robert Tibshirani, and Jerome Friedman

Pages – 767

Publisher – Springer

Format – Hardcover/Kindle

If you want to learn machine learning from the perspective of stats, this is a must-read, as it emphasizes mathematical derivations for the underlying logic of an ML algorithm. Although you should probably check you have a basic understanding of linear algebra to get the most from this book.

Some of the concepts covered here are a little challenging for beginners, but the author handles them in an easily digestible manner, making it a solid choice for anyone that wants to understand ML under the hood!

• Covers feature selection and dimensionality reduction
• Learn about logistic regression, linear discriminant analysis, and linear regression
• Dives into neural networks and random forests

10. Pattern Recognition and Machine Learning

Author(s) – Christopher M. Bishop

Pages – 738

This is a great choice for understanding and using statistical techniques in machine learning and pattern recognition, meaning you’ll need a solid grasp of linear algebra and multivariate calculus.

The book also includes detailed practice exercises to help introduce statistical pattern recognition and a unique use of graphical models to describe probability distributions.

• Learn techniques for approximating solutions for complex probability distributions
• Covers Bayesian methods and probability theory
• Covers supervised and unsupervised learning, linear and non-linear models, and SVM

11. Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications

Author(s) – Chip Huyen

Pages – 386

Format – Kindle/Paperback/Leatherbound

This is a comprehensive guide to designing production-ready machine learning systems, making it ideal for developers that need to run ML models right away.

To help you get up to speed quickly, this book includes a step-by-step process for designing ML systems, including best practices, real-world examples, case studies, and code snippets.

• Covers data cleaning, feature selection, and performance evaluation
• Learn to quickly detect and address model issues in production
• Covers how to design a scalable and robust ML infrastructure

12. Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)

Author(s) – Kevin P. Murphy

Pages – 1096

Publisher – The MIT Press

Format – eTextbook/Hardcover

This machine learning book is written in an informal style with a combination of pseudocode algorithms and colorful images. 

It also emphasizes a model-based approach, and unlike many other machine learning books, it doesn’t rely on heuristic methods but rather it uses real-world examples from various domains.

• Learn techniques for understanding and implementing conditional random fields
• Covers image segmentation, natural language processing, and speech recognition
• Utilizes Python, Keras, and TensorFlow

13. Bayesian Reasoning and Machine Learning

Author(s) – David Barber

Pages – 735

This is a comprehensive machine-learning guide that covers everything from basic reasoning to advanced techniques within the framework of graphical models. It includes multiple examples and exercises to help students develop their analytical and problem-solving skills.

It’s also an ideal textbook for final-year undergraduate and graduate students studying machine learning and graphical models. It also offers additional resources like a MATLAB toolbox for students and instructors.

• Covers basic graph concepts like Spanning trees and adjacency matrices
• Learn various graphical models like Markov Networks and Factor Graphs
• Provides an overview of statistics for machine learning
• Final Thoughts

And there you go, the 13 best machine learning books to read in 2024, with a range of machine learning books for beginners and experienced professionals.

As we continue to see an exponential expansion of data generation, machine learning continues to be in high demand by organizations that want to extract value from their datasets.

By taking the time to review our recommended machine learning books, you should be able to find a range of machine learning books that align with your career goals and preferred learning style.

Whichever book you choose, we wish you luck as you continue your journey into the world of machine learning. 

Happy reading!

Are you new to data science and machine learning but unsure where to start? Check out:

Dataquest’s Career Path for Data Science with Python

• Frequently Asked Questions

1. What Book Should I Read for Machine Learning?

Picking the best book to learn machine learning is tough, as it depends on your current skill level and preferred learning style. We’ve included a range of ML books that should be helpful for beginners along with intermediate and advanced learners. If you’re a complete beginner that wants a good book for machine learning, consider Machine Learning for Absolute Beginners .

2. Should I Learn AI First or ML?

Seeing as ML is a subset of AI, it makes the most sense to start with ML before trying to learn more advanced AI topics like deep learning or NLP. Plus, starting with machine learning and the fundamental concepts gives you a good base to dive into other AI specialisms.

3. Can I Learn ML by Myself?

Yes, you can definitely learn ML by yourself, and you should consider starting with our list of ML books to find the best book for machine learning that suits you. Another solid option is to take an ML course, like this machine learning course from Dataquest . Lastly, it can also help to seek guidance and mentorship from experienced practitioners in the field.

4. Is AI or ML Easier?

This depends on your existing skills, knowledge, and background. When it comes to AI and ML, you’ll need a mixture of technical skills, including math and calculus, programming, data analysis, and strong communication skills. Overall, it’s not really a case of which is easier, but more that they can both be challenging to learn, with ML being a natural stepping stone to learning more AI topics later.

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Simran works at Hackr as a technical writer. The graduate in MS Computer Science from the well known CS hub, aka Silicon Valley, is also an editor of the website. She enjoys writing about any tech topic, including programming, algorithms, cloud, data science, and AI. Traveling, sketching, and gardening are the hobbies that interest her.

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//Go With Code/

Helping people learn coding and tech

The 12 best machine learning books for all skill levels (2021).

Machine learning is one of the most exciting technologies in today’s world. It pays really well too ( ~$150k/yr according to Indeed ).

Wait, machines that can learn? Are we talking The Terminator??

No. Well sort of, just not walking, killing machines quite yet. And hopefully that never happens. Although Robots are starting to dance pretty well.

Don’t let the name fool you, Machine learning sounds complicated but we’re talking about software programs learning from input (like a set of data or images) and then taking some intelligent action as a result.

The good news is that having a good machine learning book can make all the difference between getting it and struggling until you give up.

And if you’re looking to get into machine learning or are curious about the field, a good book makes a great starting point.

Whether you’re a beginner, looking to delve into deep learning or neural networks or use machine learning for algorithmic trading, there is something for you among the top rated machine learning books below.

Disclosure: This article includes affiliate links that may provide a commission to me at no cost to you if you make a purchase through them.

Table of Contents

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Machine learning for absolute beginners: a plain english introduction, ai and machine learning for coders: a programmer’s guide to artificial intelligence, the hundred-page machine learning book, building machine learning powered applications: going from idea to product, the master algorithm: how the quest for the ultimate learning machine will remake our world, introduction to machine learning with python: a guide for data scientists, learning tensorflow.js: powerful machine learning in javascript, machine learning for kids: a project-based introduction to artificial intelligence, machine learning for algorithmic trading, deep learning with python, neural network projects with python, what are the best machine learning books.

Author: Aurelien Geron

Where to Buy

Hands down, Hands-On Machine Learning is the best machine learning book you can read if you’re new to machine learning, deep learning, and neural networks.

You will get a hands-on experience of the concepts, tools, and techniques you will be using to implement machine learning in your projects.

Who Is It For

Hands On Machine Learning is geared towards python programmers with close to zero knowledge of machine learning.

As a prerequisite, you need to be comfortable with python and its data science libraries like NumPy, Pandas, and Matplotlib.

What You Will Learn

A range of machine learning tools ranging from the simplest like linear regression to advanced deep learning concepts.

You will also learn how to use Scikit-Learn, Tensor Flow, and Keras libraries for machine learning projects and neural networks.

Whether your goal is to make a DIY robot smart, train a neural network, or mine hidden gems from company data, Hands-On Machine Learning will show how.

• Color illustrations make it a fun read
• Covers recent developments in the field
• Hands-on approach with minimal theory
• Excellent coverage of the field
• Clear explanations in simple English
• Great reference for machine learning projects
• Not for the absolute beginner in coding as it require basic python experience
• Not a light read – comes in at almost 800 pages

Author: Oliver Theobald

Machine Learning For Absolute Beginners is the best first machine learning book for a beginner programmer to read.

It will equip you with the theoretical knowledge you will need before you can delve into practical machine learning projects.

True to the name, Machine Learning for Absolute Beginners is designed for newbies in both programming and machine learning.

No coding experience assumed! The core algorithms are explained clearly in plain English so you won’t get lost.

The book provides a high level overview of all the basic concepts, models, and algorithms of machine learning.

Beyond this, it will also give you some basic practical knowledge to get started with machine learning.

• Perfect overview for complete newbies
• Practical and high-level intro to ML
• Clear explanations in plain English
• A quick read with a structured approach
• Visual examples make it engaging and easy to follow 
• Not for anyone past the beginner stage

Author: Laurence Moroney

If you already know how to code and looking for a quick way to get started building machine learning models, AI and Machine Learning for Coders is the book for you .

The best thing about it is that it’s written by a gifted teacher who has an uncanny ability to explain complex concepts in an easy to understand manner.

AI and Machine Learning for Coders is developer focused.

It’s especially ideal for python and mobile developers, as a basic understanding of Python, Swift, and Kotlin is a prerequisite.

To equip you with practical knowledge and give you the confidence you need to get into a career in ML, the book will show you how to tackle the most common scenarios in ML.

These scenarios include natural language processing, computer vision, and sequence modeling. You will learn how build models with TensorFlow and how neural networks work.

• Very comprehensive yet concise
• Hands-on approach with code samples
• Explains complex concepts clearly
• Prepares you for real life scenarios
• Not suitable for complete coding newbies as it requires basic knowledge of Python, Swift, and Kotlin

Author: Andriy Burkov

For a quick introduction into machine learning, The Hundred-Page Machine Learning Book will give you a solid introduction to machine learning in about 100 pages.

This makes it an ideal first read if you’re venturing into the field or just interested in getting a broad view of the field.

Anyone interested in machine learning will find this book useful.

Newcomers will get a solid introduction into all the fundamentals while everyone else will appreciate the valuable insights from a world class practitioner.

Despite its compact size, the Hundred-page book covers all the major machine learning approaches, starting from linear and logistic regression all the way to deep learning.

• Paperback version is in full color
• Concise introduction and a quick read
• Great for beginners as well as practitioners
• Both practical and theoretical elements
• Robust coverage and clear writing style
• Coming at slightly over 100 pages, some topics would benefit from deeper coverage

Author: Emmanuel Ameisen

Building Machine Learning Powered Applications is an outstanding machine learning book that will guide you through the real world implementation of ML applications.

If you are looking for practical advice on how to take ML models from idea to production, you can’t go wrong with this one.

Anyone looking to understand the end-to-end process of developing real world machine learning applications will find the book a worthy read. It does require some familiarity with Python syntax.

This book will take you through the entire end to end process of building machine learning systems.

From refining the problem, developing the program, debugging, to deploying to customers and monitoring.

You will build an example ML application from scratch and learn the tools, best practices, and challenges encountered in the process.

• Describes the entire ML pipeline
• Highlights the best practices
• Easy to read and grasp
• Features interviews with industry leaders
• Uses an example project as a case study
• A lightweight overview, doesn’t go very deep

Author: Pedro Domingos

Ever wonder how AI and machine learning will change your life and what negative implications it might have?

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World is the book to read to learn about the current status of machine learning and what the future might have in store for us.

The Master Algorithm is a highly inclusive popular science book written for the general audience.

The language is accessible to a general audience. And the content is rigorous enough for more experienced practitioners.

The book explains the five machine learning approaches and how these styles can be combined to create a master algorithm capable of solving any problem you throw at it. It also takes a look at the dark side of ML.

• Accessible to a general audience
• Doesn’t use heavy jargon
• An interesting and insightful read
• Delves into philosophical issues
• I think the book could use more diagrams and more in depth explanations.

Author: Andreas C Muller & Sarah Guido

The name says it all. Introduction to Machine Learning with Python is a fantastic introduction to machine learning in Python.

It introduces not just the theory but also the implementation. It will teach you practical ways to build your own machine learning solutions using Python.

Anyone looking to get started in ML with Python will benefit from the clear and non-overwhelming explanations.

A heavy programming background isn’t required but having a bit of Python, NumPy, and matplotlib libraries knowledge helps.

You will get an understanding of Machine Learning from the bottom-up, a step at a time. Core ML concepts are elaborated clearly.

This book will also show you how various machine learning algorithms work and how to put them into practice using Python and the scikit-learn library.

• Well designed examples and visual explanations
• Has an intuitive structure
• Covers both the concepts and implementation
• Easy for a beginners to follow
• Does not presume heavy programming skills
• More complex topics lack detailed explanations

Author: Grant Laborde

Unlike the other books on this list, Learning TensorFlow.js is all about building AI driven web applications.

It will show you how to build and deploy deep learning systems with TensorFlow.js.

Are you a JavaScript web developer looking to venture into machine learning? This book is for you.

If you’re an AI specialist, on the other hand, the book will show how to apply JavaScript to bring your skills to client browsers and the Internet of Things (IoT).

Learning TensorFlow.js starts with an introduction to machine learning and then shows you how to use TensorFlow.js to create AI driven web apps.

It will also take your hand and walk you through tensors.

• Excellent resource on Learning TensorFlow.js
• Written for web developers and AI specialists
• Entertaining and educational
• Provides practical skills
• You do need to be comfortable with modern JavaScript to follow the book

Author: Dale Lane

Machine Learning for Kids is a fun and exciting way to introduce kids to the exciting world of machine learning.

Machine Learning for Kids is rated for children of ages 12+, even if they don’t know how to code.

Kids get to develop fun AI powered games and apps using the kid friendly Scratch program .

What Kids Will Learn

Through a practical approach, kids will learn how to teach machine learning systems to recognize text, images, numbers, and sounds. This will pique their interest in this computer science field.

• Fun and intuitive teaching method
• Uses the Scratch programming language
• Interesting projects to complete
• Easy to follow exercises
• None we can think of!

Author: Stefan Jansen

Machine Learning for Algorithmic Trading is the ultimate book on applying Machine Learning for algorithmic trading .

It’s one of the most comprehensive book on the topic and it covers both trading and ML fundamentals.

If you’re interested in getting hands-on machine learning knowledge for trading, this book is for you.

But it’s not just for traders. Anyone that uses predictive algorithms in data science and analytics will benefit from the book.

At least a basic understanding of Python and machine learning is a prerequisite.

You will learn how to use data and machine learning algorithms to design your own trading strategies that will improve your trading outcomes and earnings.

• Well written and logically organized
• Covers both financial and ML concepts  
• Thorough with detailed examples
• Clear and concise writing style
• Applicable to all kinds of predictive situations
• A giant book, not for the faint of heart. Plus the huge number of libraries used leads to conflicts

Author: Francois Chollet

Deep Learning with Python is the best book on deep learning.

It manages to make a topic that’s as intricate as deep learning approachable.

It will give you a whole new level of clarity on the subject.

This book has great reviews by students, software developers, and machine-learning engineers. It’s a great way to get started with Deep Learning.

If you’ve already had an introduction in deep learning, this is a great book to expand your knowledge.

If you’re looking to learn Keras, this is also the book for you. Basic knowledge of Python is a prerequisite.

Deep Learning with Python will leave you with a solid understanding and a practical intuition about deep learning.

You’ll be able to use Keras to accomplish remarkable things and implement deep learning in your projects.

• Very hands on and practical
• Simplifies complex concepts
• Intuitive explanations of concepts
• Print book comes with a free eBook
• Features explanations in code, not math
• Very practical and not focused on the formulas and algorithms

Author: James Loy

Neural Network Projects with Python is an excellent way to learn how to build and run neural networks in python.

What makes it the best book on neural networks is that you will learn by doing.

This not only makes grasping the concepts easier but also means you will have a portfolio of neural network projects to showcase.

Anyone interested in Neural networks, no matter their background, will find the book an interesting and valuable read.

Knowing the basics of machine learning and neural networks will help you get the most out of the book.

You will start by understanding the fundamentals of neural networks and deep learning.

And after developing the six practical projects in the book, you will be able to build and train your own neural networks from scratch.

• Very thorough with detailed explanations
• Easy to follow step by step walkthroughs
• Takes a learn by doing approach
• Results in a portfolio of ML projects
• You need to have basic programming skills in python

One of the best ways to gain a good grasp of machine learning is to supplement the best machine learning books with a good machine learning course.

Some top rated online machine learning courses you can look into include:

• Machine learning by Stanford University
• Machine Learning A-Z™: Hands-On Python & R In Data Science
• Python for Data Science and Machine Learning Bootcamp

Mike is the creator of Go With Code and a coder at heart 🙂

Machine Learning Systems Book Recommendations – Learn How to Build and Understand ML Systems

“Good friends, good books, and a sleepy conscience: this is the ideal life.” ― Mark Twain

I hope you’re reading this blog in your pjs looking forward to a rejuvenating and healthy weekend.

I have been working on multiple projects lately, from creating Machine Learing Engineering and Machine Learning Operations courses to developing end-to-end ML systems at scale. And I have realized that often times I am either revisiting a book that I’ve read or I’m referring to a book that I just skimmed through but never got the chance to really read.

This week, I want to share with you the books that I personally feel every ML enthusiast and practitioner should read to get a sense of the breadth of ideas and depth of this field.

It is a short and crisp list covering a majority of ML topics. It should be useful both for beginners getting started and intermediate-level professionals wanting to understand the intricacies of engineering successful ML systems.

So, here we go...

#1 — Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow, 2nd Edition

By Aurélien Géron

This book is simply a work of art. I highly recommend not just reading this book but also coding along with the author.

The book is divided into two parts — the first part is focused on the fundamentals of machine learnin g and covers all the major classic ML algorithms. It has just the right amount of mathematical explanation and Python code to actually start developing models.

The the second part focuses on neural networks and deep learning .

I have read through this complete book and have maybe read a few chapters two or three times in order to get the concepts right and do the exercises.

Reading tip for this one: Spend 2–3 days (or more if needed) with each chapter if you’re spending 2–3 hours learning actively.

#2 — Machine Learning Engineering

By Andriy Burkov

Andriy has done it again. This book explains each phase of the ML Systems Lifecycle and is a complete and concise resource for anyone who intends to build scalable ML-powered applications.

The book is a compilation of engineering challenges and best practices to make ML work in production. Andriy explains how you should look to plan a project, why projects might fail, and how to approach every step . Here are the sections in this book:

• Before the Project Starts
• Data Collection and Preparation
• Feature Engineering
• Supervised Model Training
• Model Evaluation
• Model Deployment
• Model Serving, Monitoring, and Maintenance .

His first book,   The Hundred-Page Machine Learning Book , was a great success and the same can be said about this one as well.

#3 — Practical Deep Learning for Cloud, Mobile, and Edge

By Anirudh Koul, Siddha Ganju, and Meher Kasam

The book follows the practical advice that you should learn by doing. It's a hands-on guide to building Deep Learning applications for the cloud, mobile browsers, and edge devices. I am currently reading this book and I am surprised that I didn’t stumble upon it before.

Every chapter helps you build an application end-to-end. Each application targets a subdomain of deep learning, a different serving method, or techniques to optimize experimentation using TensorFlow.

It's a must-read for people already familiar with deep learning. This book helps you dive deeper and learn by building a set of cool projects.

#4 — Building Machine Learning Pipelines

By Hannes Hapke and Catherine Nelson

After reading a number of case studies on how organizations like Spotify and Airbnb are using TF Extended to improve their ML platforms, I started learning about TFX. It can really help you optimize the development of end-to-end pipelines.

The book explains techniques to set up ML pipelines right through from data ingestion to pipeline orchestration using Airflow or Kubeflow. TFX along with TF offers tools for every step of the process.

This is an advanced-level read, and you should indulge only after you are done reading the top two recommendations.

Interesting Read of the Week

This is a slightly unusual recommendation compared to what I usually write about. But I couldn’t resist sharing it with you because of the sheer quality of the work here.

Do you understand how an internal combustion engine works? How all of these parts come together to power your vehicles and machines? Well, I have come across the best possible explanation of the functionality of all the basic engine parts.

Read the article: Internal Combustion Engine by Bartosz Ciechanowski

The beautifully designed, 360 degree, in-action illustrations along with the explanation not only help you understand combustion engines, but for my part it definitely inspired me to work harder on my art.

This made me question whether our education system is doing enough to inspire us or are they just getting away with “teaching” us.

Thanks for reading!

That’s it for this week. I don’t want to bog you down with a plethora of random ML books. Feel free to reach out if you have any thoughts, recommendations, or questions.

If this tutorial was helpful, you should check out my data science and machine learning courses on Wiplane Academy . They are comprehensive yet compact and helps you build a solid foundation of work to showcase.

Web and Data Science Consultant | Instructional Design

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Machine Learning

Find the best machine learning books.

Machine learning books are a great resource to pump up your knowledge, and in our experience usually explain things better and deeper than online courses or MOOCs .

Once you are comfortable with Python and with Data Analysis using its main libraries, it is time to enter the fantastic world of Machine Learning: Predictive models, applications, algorithms, and much more.

There are a lot of books out there that try to teach you Machine Learning; here we have only listed some of the best ones.

Enjoy them and welcome to the beautiful world of Artificial Intelligence, Deep Learning, Natural Language Processing and in general Machine Learning to the hand of these amazing books .

The 100 Page Machine Learning book

Before getting into more extensive coding ML books, we wanted to offer a book that is more related towards giving the readers an understanding of the main topics of Machine Learning and artificial intelligence in an elegant, clear, and concise manner.

Although there is code and maths in the book, the goal of the 100 Page Machine Learning book by Andriy Burkov is to provide a common ground for any kind of person with an STEM background to meet the wonderful world of Data Science.

It covers an amazing variety of topics but not in the depth that might be offered by other books (take into account it is only a little more than 100 pages), but it does so in a simple and clear manner, and it is useful for Machine Learning practitioners as well as for newcomers to the field.

In conclusion, a perfect aperitif to start learning about this wonderful topic. Read the full review here .

Hands-On Machine Learning with Scikit-Learn & TensorFlow

Hands-On Machine Learning with Scikit-Learn & Tensorflow is thought for beginners in Machine Learning, that are looking for a practical approach to learning by building projects and studying the different Machine Learning algorithms within a specific context.

After completing the whole book you should be ready to face a project by yourself and be confortable with the different steps in this process. You will be able to code most if not all of the machine learning algorithms in Python, and understand what you are doing through the whole process. Read the full review.

Python Machine Learning

Python Machine Learning by Sebastian Raschka is one of the best books for learning how to implement Machine Learning algorithms. It does a great job introducing the theory and main concepts behind the most known Machine Learning algorithms, and the standard Data Science pipeline.

However, its main strength, and what makes the book a great companion in the learning career of any Machine Learning enthusiast, is the great practical implementations and detailed code explanations it includes. Find the full review here.

An Introduction to Statistical Learning

An Introduction to Statistical Learning provides the perfect introduction to the intersection between statistics and machine learning, covering topics that go from the most basic like linear regression to more advanced like Support Vector Machines and clustering techniques.

As the goal of the book, aside from teaching the main theoretical concepts and statistical foundations behind these techniques is to present them in a practical and applicable manner, each chapter contains a tutorial on implementing the analysis methods and prediction techniques in the R programming language. Read the review and find out if it is for you!

The Elements of Statistical Learning

The Elements of Statistical Learning  is the go-to book where many top academics will point when asked which is the best machine learning book about the theory, concepts, and workings of the algorithms and techniques.

 It is neither a beginner nor a practical book: it is the text that will get you from implementing Machine Learning algorithms to becoming an expert on the guts of all the models and techniques. Find the full review here!

Building Machine Learning Powered Applications

Machine Learning is a very beautiful theoretical field, and it’s powers and benefits are completely out of doubt. However, if Machine Learning models are not deployed to a productive environment and applications are built, these models serve no practical purpose.

The goal of  Building Machine Learning Powered Applications  is to explain in detail how to exploit these Machine Learning models to create beautiful, efficient, and useful applications and products that can provide real value.

If you want to go from theory to product, Building Machine Learning Powered Applications is one of the best available books for it. Find the full review here.

Introduction to Machine Learning with Python

Introduction to Machine Learning with Python  is a very practical book, oriented for readers who are comfortable programming in Python, and that want to learn Machine learning in a practical way, sliding away from heavy maths and complex theory.

It’s main focus is to teach programmers how to build Machine Learning applications using Scikit-learn , Pandas , Numpy and Matplotlib , in a way that is easy to follow and very hands-on, while briefly discussing the main concepts and terminology behind the Machine Learning algorithms it discusses. Find the full review here.

Deep Learning from Scratch

Deep Learning from scratch is the perfect book for those with Machine Learning, Python, and Math knowledge that want to get a profound knowledge fo the nitty gritty details of how Artificial Neural Networks work.

It will teach you so by explaining all the different concepts like the layers, back and forward propagation, metrics, and different elements step by step and with very good visual, code and mathematical explanations. This kind of learning will allow you to later build a knowledge of advanced topics with ease, and to face any problem that can be tackled with a neural network with confidence and clarity. Find the full review here .

Deep Learning by Goodfellow et al

Deep Learning by Goodfellow et al is the reference go to book in universities for teaching the theory behind Deep Learning. It is an exhaustively written book, with lots of theory and maths, oriented towards granting its readers a deep understanding of what happens in the guts of a Deep Artificial Neural Network. Because of this goal, the book contains very little code or programming references.

It is no surprise then, that this book was written by 3 of the top personalities in the world of Deep Learning:  Ian Goodfellow ,  Yoshua Bengio  (the Godfather of Deep Learning) and  Aaron Courville . It is the go to book if you want to become an expert on Deep Learning. Find the full review here.

Grokking Deep Learning

Grokking Deep Learning is a great introduction to Deep Learning that will teach you how to build Deep Neural Networks from scratch by using a first principles approach and getting you to code and understand the most basic building blocks of ANNs with very little math.

Begin your Deep Learning journey with one of the best books out there with Grokking Deep Learning. Read the full review here!

Foundations of Deep Reinforcement Learning

Foundations of Deep Reinforcement Learning  is in our opinion the best book out there to get started on the topic of reinforcement learning. It provides an introduction to Deep RL that has both, greatly explained theory, and neat code implementations.

By the end of it you will know the theory and main concepts behind Deep Reinforcement Learning algorithms, how to implement them, as well the best practices and practical details of how to get RL to work. Find the full review here!

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning is an advanced book, for graduates or Phds that already have experience in Machine Learning and Probability theory, and that are looking to deepen their knowledge in these topics through a Bayesian perspective.

If you are looking for a fun, well redacted, illustrated and complete book to master Bayesian Machine Learning, then definitely check it out. Find the full review here!

Practical Natural Language Processing

Practical Natural Language Processing provides a high overview of Natural Language processing that can suit both people with technical experience in the Data Science field, and those with no programming or tech background that want to understand how leveraging text data and data science can evolve their organisations and businesses to the next level.

Its is a text that provides a great introduction to NLP, and that is understandable to all audiences, not just techies. We love it. Check out the full review here!

Deep Learning for coders with FastAi and Pytorch

Under the slogan ‘ Make Neural Networks Uncool again ‘  fastai  is trying to democratise how the most valued weapon of Machine Learning is met by every day users.  This awesome book  demonstrates that any programmer with some  Python  experience can get amazing results using Deep Learning with very little math background, and a minimal time investment .

As so, it is mainly oriented towards coders with little experience of Machine or Deep Learning. We highly recommend it after some more general books on this section like Hands-On Machine Learning with Scikit-Learn & TensorFlow that want a practical approach to learning DL. Check out the full review!

Feature Engineering for Machine Learning

Feature engineering is a crucial step in any Data Science/ML Pipeline, however most texts are dedicated to model building and training (the 20% of the previous paragraph), rarely covering the topic of feature engineering on its own.

Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists covers the techniques for extracting and creating features out of raw data, so that your machine learning models can better understand them and therefore achieve better results. Read the full review here!

Generative Deep Learning by David Foster

Generative Deep Learning is the core of technologies like GANs and poetry writer or pyschodelic image generators. Deep Fakes are also made using it. However, despite all the power of this technology it is hard to find good resources to learn about it.

With Generative Deep Learning we think we have found the best single, unified resource to Learn Generative Deep Learning, both from a theoretical and practical side. Check out our full review here!

Data Science in Production by Ben Weber

Data Science in Production: Building Scalable Model Pipelines with Python provides a hands-on approach to scaling up Python code to work in distributed environments in order to build robust Data Science pipelines.

It will take your theoretical Machine Learning model building knowledge to the next level, teaching you how to take your analytical models to a productive environment from where real value can be extracted. Check out our full review here!

Introducing MLOps

Introducing MLOps: How to Scale Machine Learning in the Enterprise offers a very light introduction to the world of Machine Learning Operations, so important nowadays to take trained machine learning models, efficiently deploy them into a production environment and monitor their performance.

It is not a very comprehensive text, so if you want to go deep into the topic you’re better off with some other material (check the full article to see which), but if you want a quick read, intro to the topic, then this is your text. Check out the full review!

Make your own Neural Network

Make Your Own Neural Network is a fun and relaxed journey through the main concepts of Artificial Neural Networks, starting from very simple ideas and gradually building an understanding of how neural networks work.

You don’t need any complex mathematics to understand the text, nor programming experience – you will learn to code in Python and make your own neural network from scratch. Check out the full review here!

Machine Learning with Pytorch and Scikit-Learn

Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python is a book that covers the core concepts of Machine Learning going in depth into specific frameworks or libraries like  Pytorch  and  Scikit-learn with advanced topics like Q-learning and Graph Neural Networks too. It is mainly focused on how to create Artificial Neural Networks of all sort using the popular Pytorch framework.

It is a long and magnificent text that covers everything in detail, provides very illustrative figures, and amazingly comprehensive Python  code snippets . You can find a full review here: Machine Learning with Pytorch and Scikit-Learn.

We hope you liked our Machine Learning books section, enjoy, keep learning, and have a great day!

If you want to contribute and post a review here, send us an email to [email protected] .

Thank you very much for reading our blog, we hope it serves you well. Have a great day!

Tags : Machine Learning Books, The Best Machine Learning Books, Machine Learning Books for Beginners, Machine Learning Books Reviews, Machine Learning Books 20201 , ML Books.

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Technology » Artificial Intelligence/AI Books

The best books on machine learning, recommended by eric siegel.

The AI Playbook: Mastering the Rare Art of Machine Learning Deployment by Eric Siegel

Machine learning uses data to predict outcomes, explains Eric Siegel , a former professor at Columbia who now advises companies on deploying it in their business. Unlike artificial intelligence, it's a real technology with a proven track record, he says. He recommends practical books on machine learning that are accessible to the layperson and useful to anyone looking to use it in their business or organization.

Interview by Sophie Roell , Editor

Evil Robots, Killer Computers, and Other Myths: The Truth About AI and the Future of Humanity by Steve Shwartz

Mining Your Own Business: A Primer for Executives on Understanding and Employing Data Mining and Predictive Analytics by Gerhard Pilcher & Jeff Deal

Digital Decisioning: Using Decision Management to Deliver Business Impact from AI by James Taylor

The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement by Andrew Guthrie Ferguson

Handbook of Statistical Analysis and Data Mining Applications by Robert Nisbet, John Elder, Gary D. Miner

1 Evil Robots, Killer Computers, and Other Myths: The Truth About AI and the Future of Humanity by Steve Shwartz

2 mining your own business: a primer for executives on understanding and employing data mining and predictive analytics by gerhard pilcher & jeff deal, 3 digital decisioning: using decision management to deliver business impact from ai by james taylor, 4 the rise of big data policing: surveillance, race, and the future of law enforcement by andrew guthrie ferguson, 5 handbook of statistical analysis and data mining applications by robert nisbet, john elder, gary d. miner.

B efore we get to the books you’re recommending—and apologies for asking something you’ve had to answer hundreds of times—could you explain what machine learning is?

Machine learning learns from data to predict. In established company applications, that means to help target marketing, fraud detection, ads, etc. The title of my first book is Predictive Analytics , which is basically a synonym for machine learning. The subtitle is an informal definition: The Power to Predict Who Will Click, Buy, Lie, or Die . The book on the table today, The AI Playbook: Mastering the Rare Art of Machine Learning Deployment , is my second book.

Most of the media attention these days is on generative AI. It’s still the same underlying technology, but instead of predicting on that level (per customer, or per corporate client, or per satellite that might run out of battery), you’re predicting ‘what should the next word be in this sentence?’ But the core technology—of learning from data to predict—is, broadly speaking, the same.

How does that tie in with artificial intelligence ? What’s the difference between AI and machine learning?

Machine learning is a real technology with a proven track record and a true value proposition, whereas AI is the brand we hear about. It’s a subjective word that people use in many different ways, and no one can agree on the definition. Generally, AI conveys at least a little bit of overpromising, that is to say: hype. It’s basically a brand on top of machine learning.

Wow. OK. So in terms of the books you’ve chosen for today—what is it you’re trying to get across with these recommendations?

In general, the focus of my career—what I think is important—is getting value from machine learning. It’s deploying it for concrete, real uses, so that it’s actually helping organizations improve efficiency, or helping consumers, or some combination of the two.

The majority of books are either this highfalutin’ overhype with abstract buzzwords and a lot of wild promises about where we’re headed, or they’re just super technical and meant for data scientists. The in-between land of concrete, practical, meaningful, and yet accessible, entertaining, interesting books—that’s where we need more books, and that’s the main focus I had here. I did include one technical book, that’s my final choice, but my interest is in bridging a gap that’s so often unbridged.

Most new machine learning projects fail to achieve deployment. It’s an organizational failure. There’s a business practice—that’s the ‘playbook’ in the title of my new book—that needs to be followed. It brings together the tech and the business—the quants, the data scientists, the business stakeholders, their clients, their bosses—so that they get on the same page, speak the same language, and collaborate deeply in order to create value by making real change.

“There are a lot of AI hype books…They’ve been driving me bananas for decades”

The only way that what you’ve learned from data can be useful and valuable is if you act on it. That means changing operations, and that’s what I mean by deployment. It’s not just number crunching, but the actual use of models that make predictions. Those predictions then directly inform: Who should I market to? Which credit applicant should I approve? Which ad should I display? Which satellite should I inspect as potentially being low on battery? Should I drill for oil here? Whatever it is—there are a million examples.

That’s what’s so severely lacking. There is no standardized business practice well-known to business professionals. I offer that practice. I call it ‘bizML’ in my book. Two of the books I’ve chosen really are pioneering books in that realm, offering the business perspective, covering the technology and not just core number crunching for its own sake.

Because sure, the science is really cool and that’s why I got into it—that’s why most data scientists get into it in the first place. In my case, it’s been thirty years. I’ve had three decades to recover and realize, ‘Wait a minute. It’s not just the cool number crunching, let’s put the pedal to the metal and get people acting on the output.’

Your book is aimed at management, but your ideas aren’t necessarily just for companies. Could it be for government, or any organization that’s looking to use machine learning?

Let’s go through the books. First on your list you’ve got Evil Robots, Killer Computers, and Other Myths (2021) by Steven Shwartz. Tell me more.

There are a lot of AI hype books. I didn’t include any of those in my list. They’ve been driving me bananas for decades. Then, there are a bunch of generally less popular myth-buster books. This is my favorite one. There’s one in French that’s called L’Intelligence artificielle n’existe pas . I thought that was hilarious.

This book, I think, does a great job. It’s not just a mythbuster; it’s a general introduction for newcomers, lay readers, and business readers with any background to understand, and put perspective on the hype and the overpromises. Of the mythbuster books—of which there are a dozen or so that I’m aware of—this is the one I like best.

He argues that we don’t need to worry about artificial general intelligence or AGI…

Yes, and I say the same thing. AGI is where the computer can do anything a person can do and we basically have virtual humans. I believe that’s either a ghost story or a fairy tale, depending on whether you’re being a doomer or a utopian. It’s the novel Mary Shelley would have written if she knew about algorithms. The computer is not going to come alive. The fact that it’s getting better does not mean it’s taking concrete, proven steps towards general human-level capabilities. These days it’s a lot more seemingly human-like in certain ways which are astounding, but there’s no reason to think that it’s actually approaching human-level capabilities in general, or that it’s going to get a volition of its own.

Isn’t he also making the point that the ‘It’s going to take over the world!’ fear slightly distracts from the actual problems that AI is causing in certain ways?

Yes. He’s not the only one to say that, because there are grave concerns. I’ve published a dozen or so op-eds myself (in the San Francisco Chronicle , the Scientific American blog, and other places) about those concerns. They come up in my second to last choice, The Rise of Big Data Policing , which is about the ethical issues and responsible machine learning.

Whether you’re saying AI is so powerful that it’s going to kill everybody, or put everyone out of work, or that it’s going to make a utopian society because it does everything for us, any of those stories serves the same thing. It’s hype, or criti-hype (a term somebody coined) that’s trying to almost deify it.

In general, the hype just serves to increase valuations and stock prices. It certainly does distract from the real, concrete problems that we could have in machine learning deployment, but it’s mostly meant to oversell. I don’t mean that it’s all just cynical—I think a lot of people genuinely believe it. It becomes almost a religious belief.

Do you agree with him that the threat to our jobs has also been exaggerated?

Let’s go on to the next two books: Mining Your Own Business , by Jeff Deal and Gerhard Pilcher and Digital Decisioning , by James Taylor, which you wanted to talk about together.

Yes these are both more brass tacks, about the business process and business management. They are both pioneering books that really talk about how a company is going to operationalize or deploy this technology, and what it takes from a management and executive and organizational practice perspective to make those projects work effectively.

These two books are very much pioneering in bridging that gap and writing about the technology, not just in an abstract, buzzword way, but in a concrete, specific way, about how value is delivered and how the project needs to be run, in a way that’s understandable to all readers.

That’s critical because right now, we have data scientists operating in a vacuum. They come back and they say, ‘Hey! Look, I made a predictive model.’

The executives say, ‘Oh, that’s interesting. That sounds good.’

Then the data scientist says, ‘So, are you going to use it?’

‘What do you mean, use it? You want me to make a change to the way my company is operating?’

That’s another conversation, and it ends up being a non-starter because that change wasn’t part of the plan from the get-go, but that’s what needs to be put in place. That’s the message of these books—alongside just deepening the understanding of machine learning for people who are not data scientists.

Is there a good example of the way a company or an executive should approach the use of machine learning to support their activities?

In my book, I’ve got a number of examples, including UPS. It’s an established company, more than a hundred years old, that’s very much set in its ways. It improved its delivery in the US of 16 million packages a day using a system that predicts where packages are going to need to be delivered tomorrow, in combination with a system that prescribes driving routes. They save 185 million miles of driving a year, $350 million, 8 million gallons of fuel, and 185,000 metric tons of emissions, only because the leader of the project was really aggressive from the get-go, saying, ‘Hey! Look, this isn’t just a number-crunching project. We’re going to need to change the way shipping centers across the country allocate packages to trucks and then load the trucks overnight for their departures in the morning.’

That change met a lot of resistance from above him, at the executive level, and later, when they went to actually deploy it, from people working on the loading docks, who needed to follow new prescribed assignments of, ‘Put this package here; put this package there. Regardless of where you think this package should go, put it in this other truck.’ There were a lot of trials and tribulations, but relatively speaking, they had really good forethought of what it would take to manage that change.

Change management is an established field unto itself. There’s an art to it. Everyone knows it’s hard. The problem here is that with machine-learning projects, people aren’t conceiving it as requiring it. They’re not applying the art of change management, because they see it as a technical project rather than what it should be seen as. It needs to be reframed as an operations improvement project that critically uses machine learning as part of achieving its goal.

If somebody’s reading this and they’re an executive in a company, are there businesses you think are really suited to using machine learning, and others, where you can immediately say, ‘Look, everybody’s talking about AI, but actually, in your case, it’s just not relevant’?

If an organization has large enough processes—and that applies to all large organizations, even some small ones—it’s hard to think of a place where there’s not at least a certain potential. The important thing is that they focus on a concrete value proposition rather than just thinking, ‘We’ve got to use AI! Everyone else is doing it!’

If you need to improve a large-scale operation, then the way you’re going to do that is by predicting some outcome. Who’s going to turn out to be a bad debtor? Which item rolling off the assembly line is going to need to be inspected as potentially having a fault?

Business is a numbers game. Most marketing mail is junk mail; most marketing email is spam. We can’t predict like a magic crystal ball, but we can tip the numbers game by predicting better than guessing, and that’s the value proposition. It doesn’t really hinge so much on the sector or the size of the company, but on the size of the operation.

If you’re a really small company, and you’re sending out a marketing catalog once a year during the holiday season to sell candies or gifts or something, but your prospect list is a million, you can learn from the responses you got from last year’s mailing in order to better target this year’s mailing. That’s machine learning. The opportunities abound. Most large companies are using it in certain ways, but its potential is being only partly tapped at this point.

Is one of the big issues you face, when you meet executives, that people both don’t exactly understand what machine learning is and they expect too much from it?

Yes, and that goes hand in hand with the way the word AI is generally used. There is a mismanagement of expectations and over-promising. Even when a business stakeholder has a pretty good idea, e.g. ‘Let’s predict which customers are going to cancel, in order to target retention campaigns’ (i.e. giving the right incentives to those customers to try to keep them around), it turns out they need to get a lot more detail than that, in collaboration with the data scientists, in order to make the project successful.

Let’s go on to the next book you’ve chosen, The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement , by Andrew Guthrie Ferguson. This is about some of the pitfalls of machine learning.

This is where it gets into what’s important to you ethically and philosophically and politically. In my world, I’ve got some serious concerns about the wholesale automation of discrimination by machine. These decisions that are being driven by computers are often very consequential and have impacts on lives. Who gets access to housing, to credit?  In cases where the predictions directly inform a judge’s sentencing decision or a parole board’s decision, it’s even: How long do you stay in jail as a convicted felon?

There are a couple of levels of problems. One is: does the system make a decision based directly on a protected class, like race or ethnicity or national origin? In general, no. But there are places in the law where that could be allowed, and there are examples of where it takes place.

It turns out that there are a lot of needle-nosed technical experts—even in machine learning ethics—who are proponents of allowing that type of direct discrimination, where the model output by machine learning is permitted access to those protected classes and therefore can base a decision, at least partially, directly on those factors. It could say, ‘Hey! Look, you’re black. We’re going to increase your risk score by seven points.’ That’s literally the kind of thing it could do, and it potentially would do, depending on the data from which it learns.

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Getting rid of that problem, ensuring that the model is “colorblind” in that sense and doesn’t have direct access, is, I would say, the bare minimum first step. But that doesn’t eliminate the problem because it turns out you still have what some call ‘machine bias.’ The most famous citation everybody refers to is an article by that name in ProPublica , which talks about predictive policing, where it turns out that there’s a high rate of errors. The system’s always going to make an error, just like humans do. We don’t have a crystal ball or clairvoyance. They can predict better than guessing, potentially better than humans, and potentially less biased than humans.

But, because of the state of the world today and historic unfairness, it turns out that underprivileged groups are going to suffer these costly mistakes that would unjustly keep them in prison longer or lacking approval for a credit application or housing. To put it in technical terms, it’s a higher false positive rate. It’s a model saying, ‘I’ve identified you as a positive member of this high-risk group’ when they didn’t deserve it. It’s going to turn out to make that error proportionally more often with underprivileged groups.

Your last book is the Handbook of Statistical Analysis and Data Mining Applications , by Robert Nisbet, John Elder, and Gary Miner. What’s this book for?

This is for the data scientists! How can we have a list of books without having a representative from the major class of books, which is the technical ones? There are a million of them, and a lot of them are great. They’re the bread and butter I grew up on. They’re not for general readers. Among them, this book is very comprehensive. It also has a relatively large dose of business side vantage, not just the number crunching part, so it is unique in that way. And it makes your biceps really big because it’s a huge, thick, heavy book.

Could anyone aspiring to be a data scientist pick it up?

Yes, they could. It’s a technical book, but it starts from ground zero: What does it mean to learn from data? The computer program is automatically finding patterns and formulas and, in that sense, learning from the historical data or the labeled data—what do those patterns look like? Sometimes they’re just if-then business rules, and my book gets into a little of that detail as well.

This book gets into it in a much more technical way, but it does from the get-go. If you’re already a computer programmer, you’d understand it. If you’re already relatively mathematically oriented but you know nothing about machine learning, you’d follow it.

If you’re a newcomer and you have no inclination in terms of math or technology, it would be a challenge to understand. It is meant to start from ground zero, but it assumes quantitative aptitude.

​Finally, let’s say I’m in a state of fear because I’ve read quite a few books now suggesting AI is going to take over the world. You’re now telling me not to worry. How can I evaluate who is right and who is wrong?

You’ve got to be able to tell the difference between the hype and the reality because the hype is very prevalent. If you’re a general reader, it’s about getting a good sense of what this technology is. My two books are meant to ramp people up on the specific way it works and what it’s capable of. So is Evil Robots, Killer Computers, and Other Myths.

In terms of the hype you could also read an article I wrote in the Harvard Business Review last spring: “The AI Hype Cycle is Distracting Companies.” I break down where it’s over promising, why the definition of AI is such a problem and why it matters. I’ve also written a follow-up article—that’s still forthcoming—that really tries to break down the problems with the argument that we’re headed towards AGI. It’s a myth and I list five fallacies in people’s thinking that lead them to believe this is true.

The computer is not going to come alive. When you look at what concretely it can do, it’s very cool—and it’s not nearly that scary.

February 4, 2024

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Eric Siegel

Eric Siegel  is a former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running Machine Learning Week conference series and its new sister, Generative AI World, the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery,” executive editor of The Machine Learning Times , and a frequent keynote speaker. He wrote the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die , which has been used in courses at hundreds of universities, as well as The AI Playbook: Mastering the Rare Art of Machine Learning Deployment . At Columbia, he won the Distinguished Faculty award when teaching the graduate computer science courses in ML and AI. Later, he served as a business school professor at UVA Darden. Eric also publishes op-eds on analytics and social justice.

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Book Recommendation System with Machine Learning

• May 23, 2020
• Machine Learning

Recommendation systems are among the most popular applications of data science. They are used to predict the  Rating  or  Preference  that a user would give to an item.

Almost every major company has applied them in some form or the other:  Amazon  uses it to suggest products to customers,  YouTube  uses it to decide which video to play next on auto play, and  Facebook  uses it to recommend pages to like and people to follow.

Let’s Build our own recommendation system

In this Data Science project, you will see how to build a Book Recommendation System model using Machine Learning Techniques.

You can download the data sets we need for this task from here:

Let’s start with this project

#Output (1149780, 3) [‘userID’, ‘ISBN’, ‘bookRating’]

#Output (271360, 8) [‘ISBN’, ‘bookTitle’, ‘bookAuthor’, ‘yearOfPublication’, ‘publisher’, ‘imageUrlS’, ‘imageUrlM’, ‘imageUrlL’]

#Output (278858, 3) [‘userID’, ‘Location’, ‘Age’]

To ensure statistical significance, users with less than 200 ratings, and books with less than 100 ratings are excluded.

Collaborative Filtering Using k-Nearest Neighbors (kNN)

kNN is a machine learning algorithm to find clusters of similar users based on common book ratings, and make predictions using the average rating of top-k nearest neighbors.

For example, we first present ratings in a matrix with the matrix having one row for each item (book) and one column for each user.

Now we will group by book titles and create a new column for total rating count.

Now we will combine the rating data with the total rating count data, this gives us exactly what we need to find out which books are popular and filter out lesser-known books.

Filter to users in US and Canada only

Implementing knn.

We convert our table to a 2D matrix, and fill the missing values with zeros (since we will calculate distances between rating vectors).

We then transform the values(ratings) of the matrix dataframe into a scipy sparse matrix for more efficient calculations.

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Aman Kharwal

Data Strategist at Statso. My aim is to decode data science for the real world in the most simple words.

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Hi Arman, I just began with your article on algorithmic Trading. And then after clicking on previous and next articles, I felt I found a treasure. Just great. Vinod Merchant

Thanks Vinod, Keep visiting us⭐⭐

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• Book Review: Designing Machine Learning Systems

An important part of every data science professional’s growth journey is evolving from data preparation and model training to owning the entire machine learning development cycle. The full cycle includes data ingestion, data cleaning and preparation, feature engineering, model training, model evaluation, model deployment, model serving, and model maintenance. This wider-encompassing ownership model likely requires the data science professional to improve their conceptual understanding and applied understanding across the entire development cycle. Perhaps the most important moment in this learning journey is understanding that the model training is often the easiest step in the process. Designing Machine Learning Systems by Chip Huyen provides an overview of the machine learning development cycle through a conceptual lens.

Who should read this book?

Because this book focuses more on concepts rather than a technology, I expect it to have more longevity in terms of its usefulness. Therefore, it could serve as a useful reference for a wide range of tech professionals who might not understand each page on day one. That being said, the conceptual side of machine learning development can be less approachable to the data science novice. I recommend prospective readers to have a strong understanding of the following before reading this book:

• Statistical distributions, finding the range, median, average, and mode in a data set
• How to train a simple machine learning model in any language (e.g. python, R, etc.)
• A conceptual understanding of several machine learning algorithms (e.g. linear regression, decision trees, logistic regression, etc.)
• Experience cleaning and manipulating messy data in a computer language (e.g. python, R, SQL)
• Software engineering fundamentals (i.e. work experience in programming or took a few CS courses)
• Understand low quality vs. high quality data

If you cannot confidently say that you meet the above qualifications, I would recommend starting with a less advanced book. The appropriate audience ranges from a recent STEM graduate to any experienced tech professional looking to learn more about machine learning development; however, the latter will likely enjoy the book more. Concepts are harder to understand without some real-world experience in data science.

Which Topics Are Not Covered In This Book?

Some topics of interest for machine learning enthusiasts that are outside of the scope of this book include:

• Deep dives into specific machine learning algorithms (e.g. decision trees, neural networks, logistic regression, etc.). If you are interested in this, I would recommend reading my review of The Hundred Page Machine Learning Book .
• Applying machine learning concepts with programming (e.g. python, R, etc.)
• Feature engineering and data preparation examples using programming (e.g. python, spark, etc.)

Key Lessons From This Book

This book provides many invaluable lessons that can be incorporated into any machine learning development process. Because the book is more conceptual, the reader must decide the best way to apply these lessons. Here are some of the most important ideas that I found while reading this book:

Data Leakage

“Data leakage refers to the phenomenon when a form of the label ‘leaks’ into the set of features used for making predictions, and this same information is not available during inference.” This would be equivalent to trying to guess how many fingers someone is holding up behind their back when there is a mirror reflecting the answer back to you. The mirror reveals information that should not be available if the goal is to measure how accurate of a forecaster you are.

Before reading this book, I was not aware how frequently data leakage can occur during the model development life cycle. One reason for the frequency is that data leakage is not always obvious. From personal experience, I was recently a victim of data leakage when I oversampled my imbalanced dataset prior to splitting the data. Data leakage occurred because some of the replicated records were in both the training AND test sets. I had to adjust my code so the data was split much earlier in the process.

Model Development

Machine learning model development should be a series of phases, where you layer on complexity over time. Because machine learning is inherently complex, the first phase should be trying to solve the business problem via simple heuristics rather than developing a model. If simple heuristics are insufficient, then you should try developing and deploying a simple model. Going end-to-end early provides more visibility into the process and makes it easier to identify bugs as you add more complexity. If a simple model does not meet your expectations, try optimizing the simple model with different objective functions, hyperparameter tuning, etc. If that fails to meet your expectations, try more complex models (e.g. neural networks).

While working on a recent project, the most difficult part of the development was deployment. Even once I was able to deploy the model successfully, it uncovered several additional roadblocks outside of the scope of my responsibilities. If I had instead prioritized end-to-end progress over model optimization, our team likely would have uncovered these roadblocks much sooner. The overall time until completion for the new feature might have been reduced.

Model Tracking and Storage

Because model development is an iterative process, it is essential to track information about each model in order to compare performance between experiments, reproduce models, and to help maintain it. Information you might consider tracking includes:

• Model definition – Information needed to shape the model (e.g. loss function)
• Model parameters- actual values of the parameters of your model
• Featurize and predict functions- given a prediction request, how do you extract features and input these features into your model to get back prediction?
• Dependencies- python version, packages
• Data- data used for training the model
• Model generation code- frameworks used, how it was trained, etc.
• Experiment artifacts- artifacts generated during model development process
• Tags- help with model discovery (e.g. owner)

Even with all of this information tracked, it can be difficult to reproduce any specific model due to randomness inherent in the process. However, any information that you believe will be useful for maintenance, debugging, team coordination, or model reproducibility should at least be under consideration for tracking.

While tracking is extremely beneficial, it can slow down experimentation, I would recommend having a plan to automate this process as much as possible. On a recent project, Azure Machine Learning helped me track the package versions in my production environment, which was key for debugging a version-related scikit-learn bug.

Designing Machine Learning Systems is a fantastic addition to any data science professional’s library. Chip Huyen zooms out on each step in the machine learning development life cycle by focusing on concepts rather than specific implementations. After reading this book, you will have new frameworks to help you apply best practices throughout the entire machine learning development life cycle. For many of the concepts, Chip provides additional resources for further exploration. Just remember — the best practices she discusses must be combined with knowledge outside of the scope of this book in order to apply them to a specific model implementation.

~ The Data Generalist Data Science Career Advisor

PS: Chip Huyen is teaching ML Systems Design and Strategy through the Sphere platform in an upcoming cohort. If you use my referral link , you will get \$100 in credits for the next Sphere class you sign up for.

Source: Designing Machine Learning Systems by Chip Huyen

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International Conference on Intelligent Systems Design and Applications

ISDA 2022: Intelligent Systems Design and Applications pp 535–546 Cite as

Fake Review Prediction Using Machine Learning

• C Sasikala 14 ,
• Rajasekaran Thangaraj 14 ,
• Devipriya R 15 ,
• S RajeshKumar 14 ,
• Ramachandramoorthy K. B 14 ,
• S Ramya 14 &
• K Umapathi 14  
• Conference paper
• First Online: 01 June 2023

213 Accesses

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 717))

Fake Review prediction is one of the Complex areas to be scrutinized to know the original Commands. In Recent years have seen a change in movie reviews based on the expectations and frame of mind of the audience, which is crucial to a film's success or failure. After reading the review, individuals began to watch the film, which fixed their perception of the plot. The research we conducted to create a machine learning model that can determine if user reviews on the IMBD Movie Dataset are authentic or fake is summarized in this publication. To determine which machine learning categorization approach would produce the best results, we specifically applied and contrasted them. To make it easier to understand why some approaches are preferable to others in specific situations, comprehensive explanations are provided for each of the categorization strategies. The Support Vector Machine (SVM) classifier, which had an accuracy of 89.49%, produced the best results.

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Salminen, J., Kandpal, C., Kamel, A.M., Jung, S.G., Jansen, B.J.: Creating and detecting fake reviews of online products. J. Retail. Consum. Serv. 64 , 102771 (2022)

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Department of Computer Science and Engineering, KPR Institute of Engineering and Technology Coimbatore, Coimbatore, India

C Sasikala, Rajasekaran Thangaraj, S RajeshKumar, Ramachandramoorthy K. B, S Ramya & K Umapathi

Department of Computer Science and Engineering, Centre for IOT and AI, KPR Institute of Engineering and Technology Coimbatore, Coimbatore, India

Devipriya R

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Center for Smart Computing Continuum, Burgenland, Austria

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Gabriella Casalino

University of Jinan, Jinan, Shandong, China

Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

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Sasikala, C. et al. (2023). Fake Review Prediction Using Machine Learning. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 717. Springer, Cham. https://doi.org/10.1007/978-3-031-35510-3_50

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