Best Machine Learning (ML) Books - Free and Paid - Editorial Recommendations for 2022

Machine Learning, Editorial

Best Machine Learning (ML) Books — Free and Paid — Editorial Recommendations

For the past year, we have looked at over 8,371 machine learning (ML) books [1], and we have picked what we consider to be the best paid and free books on ML in terms of technicality, ability to explain complex subjects, depth, and verified reviews.

Nowadays, we know that machine learning and its applications have become inevitable [5] for most (if not all) businesses. Hence, there is a surge of proficient machine learning engineers.

We know that machine learning can be intimidating if you are just starting your career in this domain. Therefore, if you plan to move into the scientific field of machine learning, you may find yourself overwhelmed with the wide variety of books related to machine-learning available online.

In this article, we will list some of the best books on machine learning. These books are frequently used in university courses and recommended by professors and industry experts.

Disclosure: Our editorial team at Towards AI writes authentic and trustworthy reviews and may receive a small compensation on products we select to support Towards AI’s efforts. For this article, as an Amazon Associate, Towards AI may receive a small commission from qualifying purchases made from it (at no extra cost to the buyer). For feedback, questions, or concerns, please email us pub@towardsai.net.

📚 Check out our tutorial diving into simple linear regression with math and Python. 📚

1. Machine Learning

Author(s): Tom M. Mitchell

This book is “the bible of machine learning,” written by the world-renowned Carnegie Mellon, Professor Tom M. Mitchell. If you are starting your adventure in machine learning (ML), this is probably the best book. This book is full of comprehensive theories with their code examples, including case studies of various machine learning algorithms. Apart from that, the basic pseudocoding examples give readers the ability to understand future work in depth. The topics included in the book are — machine learning concepts, reinforcement learning, introduction to machine learning, and many more. We recommend this book to everyone interested in machine learning.

Grab a copy on Amazon.

2. TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers

Author(s): Pete Warden, Daniel Situnayake

TinyML is an excellent book authored by Google engineers Pete Warden and (former) Daniel Situnayake, which shows us how to create mini-machine-learning projects on embedded devices. To enjoy most of this book, you will need a bit about machine learning and software development. However, the authors make it very straightforward and assume that readers do not have a background in either ML or software engineering.

We at Towards AI are very excited about this book because it breaks the gap and showcases how to build tiny ML applications on tiny devices, helping those with fewer resources get access to the fun that it’s to work with machine learning, and to get you, even more, excited the authors have released a free intro to the first six chapters of the book and a companion to video-tutorials on how to get the most out of the book.

Grab a copy on Amazon.

3. Hands-on Machine Learning with Scikit-Learn and TensorFlow:

Author(s): Aurélien Géron

This book is probably one of the largest in data science and machine learning, which packs fantastic knowledge. It is recommended for both beginners and experts to gain useful insights into this domain. This book has a little theory, but it has powerful examples supporting it, making it in this list. The topics included in this book are — neural networks, scikit-learn for machine learning projects, training models in machine learning, TensorFlow to build and train neural networks, and many more. We can confidently say that after going through this book, you will be able to dive deeper into deep learning and solve real-world problems.

Grab a copy on Amazon.

4. Machine Learning for Hackers

Author(s): Drew Conway, John Myles White

This book is best for beginners who have some experience in R programming. The book mainly focuses on data wrangling using R. The case studies included in the book help you build a strong foundation of machine learning algorithms. The topics included in this book are — naïve Bayesian classifier, linear regression, optimization techniques, recommendation system, and many more. To be more precise, the book does not cover the mathematical derivations of algorithms but instead focuses on machine learning algorithms’ applications.

Grab a copy on Amazon.

5. Pattern Recognition and Machine Learning:

Author(s): Christopher M. Bishop

If you have already read a few books on machine learning and are familiar with many machine learning algorithms and further improve your skills in this domain, this is the book for you. This book dives deeper into machine learning algorithms and mathematics. This book’s prerequisites include familiarity with — linear and multivariate calculus, probability distributions, and a strong foundation of a programming language. It is probably the best book to read if you are already familiar with machine learning and data science.

Grab a copy on Amazon.

6. Natural Language Processing with Python

Author(s): Steven Bird, Ewan Klein, Edward Loper

This book mostly focuses on applying machine learning techniques to solve natural language processing (NLP) problems. All those interested in Natural Language Processing (NLP) with Python should refer to this book. The writing of this book is straightforward and presented in a very tidy fashion. Moreover, the book presents code examples in Python in a precise way. The topics covered in this book are — extracting features from plain text, analyzing linguistic structure, accessing popular NLP datasets, NLTK, and many more. This book helps gain practical knowledge in NLP using the python programming language and Natural Language Toolkit (NLTK) library.

Grab a copy on Amazon.

7. The Hundred-Page Machine Learning Book

Author(s): Andriy Burkov

Is it possible to understand machine learning in just 100 pages? This book is an effort to realize the same thing. This book is recommended for both beginners and experts in machine learning as this book is written straightforwardly. The Hundred-Page Machine Learning Book is endorsed by very well known figures in the Machine Learning domain, like Peter Norvig, Aurélien Géron, Karolis Urbonas, Chao Han, Sujeet Varakhedi, Vincent Pollet, Deepak Agarwal, and many more. The book covers various topics like fundamental machine learning algorithms, supervised and unsupervised learning, neural network and deep learning, and many more. We would unquestionably recommend this book for ML enthusiasts.

Grab a copy on Amazon.

8. Introduction to Machine Learning with Python

Author(s): Andreas C. Muller, Sarah Guido

This book is an ideal option for those who want to kick start their journey in machine learning. This book provides a clear explanation of fundamental concepts in data science and machine learning with a friendly tone and illustrative examples. The best thing about this book is that the reader does not require any prior knowledge of data science, machine learning, and Python. This book contains the — fundamental concepts and application of machine learning, advanced techniques for model evaluation, representation of data, the pipeline concept, suggestions for improving your data science and machine learning skills, and many more things. This book is probably one of the best for learning machine learning with Python.

Grab a copy on Amazon.

9. Data Mining:

Author(s): Ian H. Witten, Eibe Frank, Mark Hall, Christopher Pal

This book focuses on the technical aspect of machine learning algorithms. Data mining fundamentally helps us find patterns in vast datasets to conclude. If you are interested in big data and machine learning, then this is the book for you. The topic covered in this book are — clustering, regression techniques, knowledge representation, data mining techniques, and many more. The lucidness of the content provided in the book makes it in our recommendation.

Grab a copy on Amazon.

Best Free Machine Learning Books:

1. Machine Learning Yearning

Author(s): Andrew Ng

If you are in the machine learning domain, you must have heard about this book’s author. Machine Learning Yearning is a free eBook from Andrew Ng, who teaches us how to design and build machine learning projects. Ng’s book is focused not on teaching you machine learning algorithms but on how to make these complex algorithms work in real-case scenarios.

Grab it for free on deeplearning.ai

2. The Elements of Statistical Learning

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

The Elements of Statistical Learning is a phenomenal free book with vivid data visualizations and covers a vast amount of high-quality educational information on data mining, inference, and prediction. Authored by distinguished Stanford Professors Trevor Hastie, Robert Tibshirani, and Jerome Friedman, this book understands how machine learning has become a need over the last decade in diverse fields, including medicine, biology, finance, marketing, and others. While it is broad, it teaches us many techniques, from supervised learning to unsupervised learning, helping those in their machine learning journey to work with real use case scenarios.

Grab it for free on Stanford University’s website.

3. An Introduction to Statistical Learning

Author(s): Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani

An Introduction to Statistical Learning is a fantastic resource for those who do not possess a strong mathematical background. It showcases an excellent introduction to statistical learning methodology with R and is a precious resource for machine learners. This book is critical in presenting a comprehensive and accessible resource to statistical and non-statistical practitioners who would like to use cutting edge techniques to solve complex problems with data. If you are curious about its requirements, you only need to know how to work with linear regression to make the most use of this book.

Grab it for free on USC’s website.

Conclusion:

We hope you love reading these books and gain some useful insights on machine learning out of it. If you come across any phenomenal books on the scientific field of machine learning, such as the ones mentioned in this list, please let us know by email.

Thank you for reading!

References:

[1] Machine Learning Books, Data from Amazon, https://www.amazon.com/s?k=machine+learning

[2] University of Southern California, An Introduction to Statistical Learning, http://faculty.marshall.usc.edu/gareth-james/ISL/

[3] The Elements of Statistical Learning, Stanford University, https://web.stanford.edu/~hastie/ElemStatLearn/

[4] Machine Learning Yearning, deeplearning.ai, https://www.deeplearning.ai/machine-learning-yearning/

[5] 8 Ways Machine Learning Is Improving Companies’ Work Processes, Harvard Business Review, https://hbr.org/2017/05/8-ways-machine-learning-is-improving-companies-work-processes