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Simran Kaur Arora | 08 Aug, 2023
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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 Books for Beginners

1. The Hundred-Page Machine Learning Book

The Hundred-Page Machine Learning Book

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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.

Features

  • 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

Machine Learning for Absolute Beginners: A Plain English Introduction

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Author(s) – Oliver Theobald

Pages – 179

Latest Edition – Third Edition

Publisher – Scatterplot Press

Format – Kindle/Paperback/Hardcover

Why we chose this book

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.

Features

  • 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

Machine Learning for Dummies

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Author(s) – John Paul Mueller and Luca Massaron

Pages – 464

Latest Edition – Second Edition

Publisher – For Dummies

Format – Kindle/Paperback

Why we chose this book

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.

Features

  • 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

Introduction to Machine Learning with Python: A Guide for Data ScientistsCheck Price

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

Pages – 392

Latest Edition – First Edition

Publisher – O’Reilly Media

Format – Kindle/Paperback

Why we chose this book

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.

Features

  • 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

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

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Author(s) – Aurélien Géron

Pages – 861

Latest Edition – Third Edition

Publisher – O’Reilly Media

Format – Kindle/Paperback

Why we chose this book

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.

Features

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

6. Understanding Machine Learning

Understanding Machine Learning

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

Pages – 410

Latest Edition – First Edition

Publisher – Cambridge University Press

Format – Hardcover/Kindle/Paperback

Why we chose this book

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.

Features

  • 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

AI and Machine Learning for Coders: A Programmer's Guide to Artificial IntelligenceCheck Price

Author(s) – Laurence Moroney

Pages – 390

Latest Edition – First Edition

Publisher – O’Reilly Media

Format – Kindle/Paperback

Why we chose this book

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.

Features

  • 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

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

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

Pages – 774

Latest Edition – First Edition

Publisher – Packt Publishing

Format – Kindle/Paperback

Why we chose this book

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.

Features

  • 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

The Elements of Statistical Learning: Data Mining, Inference, and PredictionCheck Price

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

Pages – 767

Latest Edition – Second Edition

Publisher – Springer

Format – Hardcover/Kindle

Why we chose this book

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!

Features

  • 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

Pattern Recognition and Machine Learning

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Author(s) – Christopher M. Bishop

Pages – 738

Latest Edition – Second Edition

Publisher – Springer

Format – Hardcover/Kindle/Paperback

Why we chose this book

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.

Features

  • 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

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

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Author(s) – Chip Huyen

Pages – 386

Latest Edition – First Edition

Publisher –O’Reilly Media

Format – Kindle/Paperback/Leatherbound

Why we chose this book

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.

Features

  • 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)

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

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Author(s) – Kevin P. Murphy

Pages – 1096

Latest Edition – First Edition

Publisher – The MIT Press

Format – eTextbook/Hardcover

Why we chose this book

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.

Features

  • 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

Bayesian Reasoning and Machine Learning

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Author(s) – David Barber

Pages – 735

Latest Edition – First Edition

Publisher – Cambridge University Press

Format – Kindle/Hardcover/Paperback

Why we chose this book

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.

Features

  • 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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By Simran Kaur Arora

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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Disclosure: Hackr.io is supported by its audience. When you purchase through links on our site, we may earn an affiliate commission.

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