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Fact checked by In this article, we share the 13 best machine learning books in 2023. 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 2023 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 2023 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
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
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
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
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
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
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
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
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
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
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
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)
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
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 2023, 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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