10 Best Machine Learning Books for Beginners and Experts

Do you want to be a machine learning expert or engineer? Then you can go with these best machine learning books. Machine learning is one of the hottest topics of 2022. It is essentially a branch of AI (Artificial Intelligence) that provides a machine the ability to make decisions without any human intervention or explicit programming.

Machine learning algorithms are built by collecting huge amounts of data from various sources and building an intelligent system (model) based on past experiences, patterns, and similar behaviors.

Machine Learning Applications

ML has found applications in various fields, with the most common being virtual personal assistants like Alexa and Siri. Recommendation engines like those of Netflix (movies), Amazon, and Flipkart (online shopping) are also powered by ML. Other than this, live traffic predictions, face detection, spam filtering, fraud detection, and digital medical assistance are some of the most powerful applications of machine learning .

Best Machine Learning Books

Books are a great way to start your machine learning journey, but you'll be overwhelmed by the list of books you will find when you do an online or offline search. The right book can keep you hooked up to the topic and let you start your ML journey in a proper manner.

We have compiled a list of our ten best machine learning books that help you learn ML from scratch. Also, they serve as a step-by-step guide to enable you to start using different ML algorithms in your code.

1. Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow

Author: Aurelien Geron

This book is a starting point for those who have no background in machine learning whatsoever. It starts with very basic concepts and explains many ML techniques in detail. The author has also added nice bits of humor to keep you engaged throughout.

The ML book maintains a perfect balance of theory and practicality in a subtle way, along with easy-to-understand tips and tricks. It doesn’t go into teaching Python, but if you are already familiar with Python, this should be your must-go book for ML.

The book focuses on the how and why of machine learning. This means you always know why you are following a particular approach when you are doing it. If you are serious about learning ML, go for this book. It is intense and practical but needs a lot of focus.

Major Highlights of Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow:

• Starts from traditional ML algorithms and moves on to deep learning and neural network techniques.
• A good balance of theoretical concepts and examples.
• All the example code given in the book is present on GitHub. Thus, it is accessible at any time.
• The book extensively uses frameworks like Scikit-Learn (first part of the book), TensorFlow, and Keras (second part).
• You should know some basics of Python libraries like Numpy, pandas, and matplotlib.
• The book also points to many other useful resources like online courses, other books, and data sources to continue your ML journey.

About the Author

Aurelien Geron is a machine learning consultant and a machine learning trainer. From 2013 to 2016, he led YouTube's video classification team at Google. Moreover, he was the founder and CTO of Wifirst from 2002 to 2012.

Publisher: Shroff/O'Reilly
Paperback Print Length: 848 pages

You can buy this book from here .

2. Machine Learning for Absolute Beginners

Author: Oliver Theobald

As the name goes, the book teaches ML from “scratch.” It is a compact ML book that can serve as a good starting point. The author knows what could be the pain points of ML and explains those carefully to the reader and with the right approach.

If you are looking for in-depth knowledge on all the ML topics, this book alone is not sufficient. Nonetheless, it does name all the topics you should be familiar with, so you can look them up and read from other resources.

Major Highlights of Machine Learning for Absolute Beginners:

• The book also offers a little introduction to Python (in the appendix). However, it will be good to learn the programming language (or know at least one programming language) before you start this book.
• The author illustrates many concepts with simple diagrams that you can relate to, leading to faster and easier understanding.
• It focuses on a strong understanding of statistics and also briefs about the challenges faced in ML.
• Though at a high level, the book covers all ML techniques, starting from regression analysis to decision trees and artificial neural networks.
• The machine learning book points to the right sources to download datasets for your own practice.
• If you do not understand ML terms and are totally new to the field, then this is the best machine learning book for you.

About the Author

Oliver Theobald is the best-selling author of many reputed machine learning books, including Machine Learning with Python, Machine Learning: Make Your Own Recommender System, Python For Absolute Beginners, and many more.

Publisher: Independently Published
P aperback Print Length: 164 pages

You can buy this book from here .

3. Machine Learning: An Applied Mathematics Introduction

Author: Paul Wilmott

This machine learning book follows an intuition-based approach to machine learning that doesn’t overload you with technical details. It is a different and likable approach that you will enjoy. ML is a vast subject and needs a lot of time and effort. However, the author makes it more interesting and relatable due to the unique approach.

Machine Learning: An Applied Mathematics Introduction covers all the ML topics, from basics to advanced, in detail. There are also many case studies and real-life examples from domains like finance, politics, business, and gambling.

Major Highlights of Machine Learning: An Applied Mathematics Introduction:

• Although the book covers a lot of topics in detail, it is still good for beginners and intermediate-level learners.
• The author maintains a friendly and witty tone throughout the book.
• If you are not yet fully convinced about learning ML and building a career in data science, this book will completely convince you to continue.
• The book doesn’t just talk about how ML algorithms work, but it also brings a bigger perspective. It explains the challenges and the problems associated with ML as of today.
• The author neatly compares classical mathematical modeling techniques with ML techniques for readers to get a good overview of what they are getting into.
• There is no actual code. You would only find a description of what has to be done. Hence, you can think of the code on your own.

About the Author

Paul Wilmott specializes in finance and mathematics. He has written over 100 research articles in the same fields. He holds a DPhil degree in mathematics from St Catherine's College, Oxford. Moreover, he is the president of the CQF Institute and the creator of the Certificate in Quantitative Finance.

Publisher: Panda Ohana Publishing

Paperback Print Length: 242 pages

You can buy this book from here .

4. Deep Learning (Adaptive Computation)

Author: Ian Goodfellow, Yoshua Bengio, and Aaron Courville

If you are new to the field of data science and ML, this may not be the book for you. That's because Deep Learning (Adaptive Computation) is fairly advanced in its concepts and assumes some experience from the reader. It focuses more on the ML concepts and covers them in-depth.

The author starts with basic topics like scalars, vectors, and linear algebra and goes on to advanced topics, primarily deep learning research. The power of this book lies in the last few chapters, which are exhaustive, intense, and focus on core deep learning methodologies. The book is replete with keywords and important terms, and easy-to-understand examples.

Major Highlights of Deep Learning (Adaptive Computation):

• If you are really interested in deep learning techniques and the math behind them, this book will be your cherished treasure.
• The initial few chapters are dedicated to basic applied mathematics. This includes linear algebra, probability, multiplication of vectors, and determinants.
• The final part of the book is dedicated to deep learning research. It is something that you will not find in any other machine learning book or study material.
• There are plenty of resources and supplementary resources available on the website (deeplearningbook.org) that complements the book.
• The book is different from other usual books that you will find on machine learning. It follows a more research and learning-based approach than implementation.

About the Author

Ian Goodfellow is a research scientist at Google. Yoshua Bengio is a Computer Science Professor at the Université de Montréal, and Aaron Courville is the Computer Science Assistant Professor at the Université de Montréal.

Publisher: The MIT Press
Paperback Print Length: 800 pages

You can buy this book from here .

5. Pattern Recognition

Author: Christopher M. Bishop

This book gets straight into the concepts of machine learning. Rather than giving a general introduction or explaining the background, Pattern Recognition explains ML concepts with simple examples. Pattern Recognition follows a clear-cut and to-the-point approach while explaining each topic.

There are a lot of graphical illustrations and formulae comprehensively covering the mathematical aspects of probability, decision theory, and information theory and distribution. Each chapter is followed by exercises that will make you think. You can also refer to the cross-references mentioned in the chapters.

Major Highlights of Pattern Recognition:

• The book dedicates separate chapters for each ML technique and thoroughly explains the models with simple and relatable examples.
• Some parts of the introduction (like concepts of the matrix and polynomial distribution) are explained well in the appendix section so that you can refer back and forth while reading those chapters. The data sets used in the chapters for illustration are also explained in the appendix (data itself can be taken from the book’s website) for practice and self-learning.
• If you are not from a mathematics background, it is better to read a simpler mathematics book on linear algebra, matrices, calculus, probability, and statistics beforehand. That's because this book doesn’t cover much of the basics. Instead, it only covers the length of the topics as required from the ML perspective.
• The book doesn’t bore you with too much theory. There are practical illustrations and graphs everywhere. You can easily get engrossed in it.
• It is a great self-learning book for intermediate ML learners.

About the Author

Christopher M. Bishop is a computer science professor at the University of Edinburgh. He is also a Microsoft Distinguished Scientist and the Laboratory Director at Microsoft Research Cambridge. Moreover, he is a Fellow of Darwin College, Cambridge.

Publisher: Springer
Paperback Print Length: 738 pages

You can buy this book from here .

6. Python Machine Learning - Second Edition

Author: Sebastian Raschka and Vahid Mirjalili

Python Machine Learning is one of the best intermediate-level machine learning books. You can still go for it as your first read if you are up for the challenge of supplementing your reading with online and offline study material and doing your own research along the way. Believe us, in this way you will learn faster and better.

This is an ideal book for software engineers and business analysts alike. It gives you a good working knowledge of all the concepts that you will need to build your career in data science. It has the perfect balance of theory and implementation.

Major Highlights of Python Machine Learning:

• There are tips and notes throughout the book as if the author already knows the points you are going to get stuck on while solving a problem!
• If you are not from a coding background, you can still get the most out of the book because of the thorough explanations and relatable examples.
• Each of the concepts like classification, regression, and linear algebra is explained with simple examples and beautiful illustrations. This makes them relatable and easy to understand.
• The book also briefs about the important packages of Python and sets up the environment for further learning.
• The ML book is organized well. The transition between chapters is natural, and the level of complexity increases gradually.

About the Author

Sebastian Raschka has many years of hands-on experience with Python. He has delivered a number of seminars on machine learning, Python, data science, and deep learning. Also, he received the ACM Computing Reviews' Best of 2016 award.

Vahid Mirjalili holds a Ph.D. in mechanical engineering. He taught Python to engineering graduates at Michigan State University. He has chosen Python as a programming language throughout his academic and research career. Hence, he has tremendous experience working with Python.

Publisher: Packt Publishing
Paperback Print Length: 624 pages

You can buy this book from here .

7. Machine Learning for dummies

Author: John Paul Mueller and Luca Massaron

Well, how can a list of best machine learning books be complete without including the Machine Learning for dummies book! The author of this awesome ML book assumes you have no background in machine learning and explains the basic concepts of ML thoroughly and citing common day-to-day scenarios as examples. The biggest advantage of the Machine Learning for dummies book is that you can get familiarized with both Python and R. The author’s style is mesmerizing and amusing.

Major Highlights of Machine Learning for dummies:

• The book starts with very simple and basic topics, and as you move on from chapter to chapter, the complexity goes up.
• It is a complete guide that covers every aspect of machine learning. What it is, why is it useful, history and background, building models, optimizing models, learning the most important packages, libraries, solutions, and real-life problems.
• There are lots of tips and additional resources scattered throughout the book that are helpful and will enhance your learning at every stage. These can be online resources like cheat sheets or code chunks present in the book for you to copy and paste. However, we would recommend you type at least a few lines of code on your own so that you get the hang of it.
• It is a good book for trying on new things with ML. Also, it is an ideal book for candidates looking to experiment with and explore machine learning on their own. The book boosts your imagination and motivates you to think out of the box.

About the Author

John Paul Mueller is a freelance author and technical editor. He has written a myriad of articles covering home security and networking to database management and heads-down programming.

Luca Massaron is a data scientist. He specializes in organizing and interpreting big data and transforming it into smart data using data mining and machine learning techniques.

Publisher: For Dummies
Paperback Print Length: 432 pages

You can buy this book from here .

8. Fundamentals of Machine Learning for Predictive Data Analytics

Author: John D. Kelleher, Brian Mac Namee, and Aoife D'Arcy

This is an amazing book. It is self-contained and practical. The machine learning book not only explains how each ML algorithm works but also explains its strengths and weaknesses, variations, and trade-offs. The concepts are beautifully explained without limiting the explanations to any particular programming language. The ML book is good for programmers as well as non-technical analysts.

Major Highlights of Fundamentals of Machine Learning for Predictive Data Analytics:

• The author builds a strong foundation of ML algorithms in a simple and engaging manner.
• Each stage of data analysis, including preparation and applying ML techniques, is explained in-depth along with practical examples.
• Less theoretical and more application-based approach.
• This book is a great starting point to build a strong foundation for your ML career.
• Case studies provide an excellent insight into all the stages of ML and data science.
• Even the smallest of concepts are explained in detail. A lot of other best machine learning books miss out on explaining topics like the metric system, binning, and normalization.

About the Author

John D. Kelleher is an academic leader of the Communication, Information, and Entertainment Research Institute at Technological University Dublin.

Brian Mac Namee is an associate professor at the School of Computer Science at University College Dublin.

Aoife D'Arcy is a CEO of a Dublin-based data analytics company, Krisolis.

Publisher: The MIT Press
Paperback Print Length: 624 pages

You can buy this book from here .

9. Deep Learning with Python

Author: François Chollet

This ML book is slightly different in its content and structure. It has two parts. Part 1 talks about machine learning principles and techniques, and Part 2 focuses on the deep learning techniques, advantages and challenges faced. Reading this book requires you to have prior experience in Python. However, it is not too technical, which means anyone can use this as a guide to learn DL.

Major Highlights of Deep Learning with Python:

• It is a good book for beginners to learn both ML and deep learning. The book’s approach transitions seamlessly from simple concepts to more advanced and complex topics.
• It is a well-organized and structured machine learning book. It is like learning ML concepts in a guided classroom course.
• The book covers several practical examples that are relatable and easy to understand. The entire source code used in the book is available on GitHub.
• The ML book focuses on using the Keras library, which is an additional plus point (Keras is one of the most popular and powerful libraries for practical purposes.).
• The author spends a good amount of time explaining basic concepts of machine learning. As you practice along, the time to understand gradually reduces because the concepts are ingrained in your mind. Thus, learning becomes rather easy despite the increase in the complexity of topics.
• The author also adds his point of view on different topics of deep learning. This becomes helpful to have deeper and more intuitive insights.

About the Author

François Chollet is a creator of a popular deep learning library, Keras. He works on deep learning at Google. Also, he is a contributor to TensorFlow, a popular machine learning framework.

Publisher: Manning
Paperback Print Length: 384 pages

You can buy this book from here .

10. A Probabilistic Perspective

Author: Kevin P. Murphy

This is a great ML book, but not self-contained. It is somewhat advanced in nature and sometimes goes beyond comprehension for absolute beginners. However, the book is very captivating and has lots of things that you wouldn’t want to miss. This includes great visuals and illustrations, loads of web references, and good examples. If you have a solid background in mathematics, you will enjoy this book thoroughly.

Major Highlights of A Probabilistic Perspective:

• The book gives in-depth knowledge about modeling and evaluation. Just think of a doubt, and you will surely find the answer in the book.
• It is a good book to get a detailed and strong foundation of all the statistics and math related to ML, so much so that you might want to train others as well.
• Focuses more on Bayesian statistics and strongly supports Bayesian theory.
• Some steps are left out for the readers to figure out. Thus, you have to be at least at an intermediate level to do that.
• The book is much more detailed, not just about the techniques but also the idea behind them, and compares different techniques so that the reader can use their judgment to use the right technique for a given problem.
• Overall, it is a good book for students, teachers, and practitioners of ML.

About the Author

Kevin P. Murphy is a Senior Staff Research Scientist at Google Research.

Publisher: The MIT Press
Paperback Print Length: 1104 pages

You can buy this book from here .

Conclusion

There are a galore of machine learning books, but we have listed the best ones above. It does not mean you have to get all of them immediately. Neither does it mean that just one or two of them are sufficient. As the field of ML is ever-evolving, our knowledge has to grow for us to be more intuitive and informative. Some basic machine learning books are self-contained.

For example, Machine Learning for Absolute Beginners and Machine Learning for dummies. But, these may not be enough if you are looking for deeper explanations.

In that case, you might want to complement these with Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow, Pattern Recognition, and Machine Learning: An Applied Mathematics Introduction. Same way, Deep Learning with Python is a great start, but it would be nicer to read this book along with Deep Learning Adaptive Computation and Python Machine Learning – Second edition .

You can choose to buy whatever combination of machine learning books you want to, based on the information given in this article.

We would recommend you to start with Machine Learning for Absolute Beginners, followed by any other intermediate- or basic-level book if you have no idea about ML or Python. But if you know mathematics or Python or a little bit of ML, Machine Learning for dummies or even Fundamentals of Machine Learning for Predictive Data Analytics would be a good choice to begin.

Happy learning!

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