10 Best Machine Learning Books for Beginner & Experts

So, you wish to gain proficiency in Machine Learning! Here’s a well-curated list featuring the best machine learning books out there. Read on, as this is your chance to be in the know.


With rapid technological advancements, automation has become mainstream. And, with the rise in automation, machine learning is gaining traction. The widespread application of machine learning makes it a must-know for anyone looking to stay on the cutting-edge of tech.

With plenty of books available on the subject, you are spoilt for choice. Thankfully, LearnDunia has compiled a list of the top ten machine learning books that are favourites of machine learning practitioners. So, just read on and feel free to pick the best option suiting your unique requirements and learning goals.

Best Machine Learning Books

1. Hands-On Machine Learning with Scikit-Learn and TensorFlow (2nd Edition)

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

Description: The book is special simply because it takes a hands-on approach to learning by doing. It begins by providing a great deal of context and practical tools to solve all kinds of problems. You can call it a code-focused book that provides ample opportunities to run working codes on real-life problems.

The best thing about the book is that it doesn’t skip the hard part of breaking complex concepts into small chunks that can be readily consumed. Also, the humorous approach by the author makes it a fun read, although the book is well over 900 pages in length.

There are also very useful tricks and tips that make this book a must-have for any Python-rooted data scientist or Machine Learning engineer.

  • Originally Published- Aurelien Geron
  • Author- 2017

You can buy this book here.

2. The Hundred-Page Machine Learning

The Hundred-Page Machine Learning Book

Description: Andriy Burkov, the author, has done an incredible job when it comes to squeezing machine learning into just a hundred-odd pages. Mind you, the author hasn’t achieved that by excluding major topics or compromising quality.

While the book does not cover everything there is to know about machine learning, it does provide an excellent introduction to the fundamentals. The explanations are concise, which is another USP of the book.

However, at times, too much information is packed into just a few paragraphs. To better understand the concepts, try re-reading the topics. Also, the diagrams provided are beneficial for the reader. It is a must-have for those interested in Machine Learning.

  • Originally Published- 2019
  • Author- Andriy Burkov

You can buy this book here.

3. Learning From Data

Learning From Data

Description: It is an excellent introductory course to Machine Learning. Coupled with a free course provided by the same authors (Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin), this book covers all things Machine Learning.

In exceptional detail and clarity, “Learning from Data” explains linear and non-linear models and how they derive from one another. The exercises are great for practice. If you don’t find the math part overly complicated, the book provides some solid insights into the learning process as a whole.

As one of the best machine learning books, it should be on your bookshelf. Each time you read it, you will gain something. However, if you are not comfortable with engineering mathematics, the book is not for you.

  • Originally Published- 2012
  • Author- Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin

You can buy this book here.

4. Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning (Information Science and Statistics)

Description: This book makes perfect sense if you looking for the best machine learning book that contains best-in-class diagrams, is well organized, and explains everything with clarity. No wonder, “Pattern Recognition and Machine Learning” by Christopher Bishop is a coveted book for Machine Learning enthusiasts.

If you have a background in probability, linear algebra, calculus, and statistics, you can get the most out of this book. However, if you lack knowledge of the basics, you will find an otherwise easy book quite difficult.

Christopher Bishop has included some excellent topics in the book. While the book isn’t comprehensive in itself, it can act as a springboard to many other advanced texts. While this book may not be the ideal choice to start your machine learning process, it is an excellent in-depth book on pattern recognition.

  • Originally Published- 2006
  • Author- Christopher Bishop

You can buy this book here.

5. Machine Learning For Dummies (1st Edition)

Machine Learning For Dummies

Description: Machine Learning For Dummies is an excellent resource to start your Machine Learning journey. You can rely on the book to get insights into a few foundational chapters that cover the basics and then applies them in R or Python.

The book begins with some introductory chapters on Machine Learning. As it moves forward, chapters deal with meatier sections of machine learning. Therefore, the book no longer remains ‘For Dummies’. You can use the book as a reference source for tools, acronyms, jargon, and concepts associated with Machine Learning.

It is easy to follow while the examples provided are self-explanatory. As with all the ‘For Dummies’ books, the book’s USP is the way it simplifies complex concepts.

  • Originally Published- 2016
  • Author- John Paul Mueller, Luca Massaron

You can buy this book here.

6. Introduction to Machine Learning with Python

Introduction to Machine Learning with Python A Guide for Data Scientists (Greyscale Indian Edition)

Description: This book is written in a way to provide you with an understanding of Machine Learning step-by-step from the bottom up. That makes it one of the best machine learning textbooks out there.

It is well written, organized, and easy to follow with hands-on examples. This book is perfect for ML practitioners, as it walks them through various uses of Machine Learning algorithms without getting into the mathematical details. The lack of mathematical information means you won’t be able to program the algorithms from scratch. But then, that’s not what you picked the book for.

  • Originally Published- 2016
  • Author- Andreas C. Müller , Sarah Guido

You can buy this book here.

7. Programming Collective Intelligence: Building Smart Web 2.0 Applications

Programming Collective Intelligence Building Smart Web 2.0 Applications

Description: This book explains when you should use the algorithms and how to implement them in Machine Learning. While the book might seem a bit outdated and written in Python 2, the concepts in the book have aged well. All of the libraries used in the book are available for Python 3.

This book has some great examples that are a joy to work with. It is more about implementing ML than anything else. Once you go through the book, you’ll be able to appreciate the relevance of Python for solving AI problems. You can call it the best machine learning python book for starters.

  • Originally Published- 2007
  • Author- Toby Segaran

You can buy this book here.

8. Understanding Machine Learning: From Theory to Algorithms

Understanding Machine Learning From Theory to Algorithms

Description: The book starts with a clear and concise introduction to Machine Learning concepts and then connects them to the algorithms. You can expect a summary at the beginning of each chapter that gives a clear sense of what will be accomplished in it. And, attention to notation makes sure that mathematics supports understanding rather than gets in the way.

The chapters in the book are short, which makes it an exciting read. It provides theoretically grounded explanations for most common learning paradigms and algorithms. The authors have highlighted the critical results in statistical learning theory, which is great for quick learning.

Another great feature of the book is that the authors explain the key ideas before presenting the results. That allows readers to understand the fundamental techniques quickly and effectively.

  • Originally Published- 2014
  • Author- Shai Shalev-Shwartz

You can buy this book here.

9. Machine Learning for Hackers

Machine Learning for Hackers Case Studies and Algorithms to Get You Started

Description: This book is another great introduction to machine learning, covering a wide range of topics. There are separate chapters for each problem in machine learning, like classification, optimization, and recommendation. The topics aren’t discussed much in-depth as the author is content with providing an overview of as many topics as possible.

The algorithms and case studies are the most important features of the book. The case studies are diverse and help in understanding the concepts. The minimal explanation of mathematical theory also encourages readers who want to avoid that part yet learn machine learning.

Although the book is more of an introduction to Machine Learning, you need some background in programming and particularly in R to get the most out of it.

  • Originally Published- 2012
  • Author- Drew Conway and John Myles White

You can buy this book here.

10. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition

The Elements of Statistical Learning Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

Description: This is a comprehensive guide to machine learning and can be used as a reference book as well. The text is full of equations necessary to the methodology without overburdening the readers with apparent proof.

The book takes a more rigorous take on machine learning than most beginner-level books. It also includes incredibly relevant machine learning tools omitted by many popular books in the category. Support Vector Machines, Random Forests, and Ensemble Learning are explained in detail.

The book is more about applying machine learning rather than learning the theory part of the topic. The linear model part could be a bit tricky, but if you have done even a single course on it before reading this book, it won’t bother you much.

  • Originally Published- 2016
  • Author- Trevor Hastie, Robert Tibshirani, Jerome Friedman

You can buy this book here.


A career in machine learning is one of the most lucrative options available as of now. If you’re looking to make forays into machine learning, complement your courses with the best machine learning book for the best results. We have handpicked ten of the best books on the subject. However, if you believe we have overlooked something, please let us know in the comments section.

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