Best Machine Learning Books: The Ultimate List

Machine learning is a very broad field. Many people are currently exploring it, varying from students and hobbyists to researchers and professionals. To help them in their endeavor, there are thousands of books available on the subject these days. There is no shortage of the best machine learning books, but they can be difficult to navigate through, especially if you’re new to machine learning.

This article aims to solve that problem by listing some of the best machine learning books that will allow you to know exactly what you’ll get from them.

The Best Machine Learning Books

Machine learning books are essential resources for learning the ML algorithms that enable us to integrate machine learning into our (AI) models. The presence of machine learning libraries in various programming languages makes it easy to implement ML methods without having to learn how the same work.

However, just reading about the mathematics involved in machine learning does not help you apply it to real-life problems. That is why there are textbooks written by some of the leading practitioners that cover all of the required aspects of machine learning. Eight of the best machine learning books are:

1. The Hundred-Page Machine Learning Book

Author – Andriy Burkov

If you want an ML textbook to get you started, this book by Andriy Burkov is what you need. The Hundred-Page Machine Learning Book is an interactive, illustrated figure book that teaches you the basics of the anatomy of a learning algorithm as well as the fundamentals of ML algorithms.

We highly recommend you read this book a lot. It will assist you with clearing ML-based interviews, construct complex AI frameworks, and even begin an ml-based business.

2. Programming Collective Intelligence: Building Smart Web 2.0 Applications

Author – Toby Segaran

Toby Segaran, the author of this book, explains a lot about the future of machine learning. The machine learning book thoroughly explains the types of machine learning algorithms, namely supervised learning and unsupervised learning.

Programming Collective Intelligence aims to explain the basic foundations of the field of ML with a lot of mathematical exercises that will help you get familiar with the mathematical proofs and examples commonly used in machine learning.

3. Machine Learning for Hackers: Case Studies and Algorithms to Get you Started

Author – Drew Conway and John Myles White

This is a good starter book for anyone who has some experience in machine learning. It provides a basic understanding of the subject. There’s also an explanation of how linear regression works and how it can be used to build more sophisticated models.

Machine Learning for Hackers is a great way to get started with learning ML in a relaxed setting. The book will make you learn the concepts and algorithms used in machine learning thoroughly.

4. Machine Learning

Author – Tom M. Mitchell

When artificial intelligence is combined with machine learning and pattern recognition, the results are amazing. This best machine learning book provides a strong foundation and leads you on a journey to learn more about the science of machine learning. It’s no surprise that the book with the most combined reviews is Machine Learning by Tom M. Mitchell.

5. The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Author – Trevor Hastie, Robert Tibshirani, and Jerome Friedman

If you need to get familiar with the sorcery behind AI calculations and how math and measurements assume an essential part in it, then The Elements of Statistical Learning is the book that you should peruse. The concepts in this ML book are very complex, which means it’s difficult to understand them from scratch. Thus, you need some ML experience to make the most out of this machine learning book.

6. Learning from Data: A Short Course

Author – Yaser Abu Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin

There are a lot of best machine learning books out there that attempt to teach the reader machine learning, but this book does something else. It prepares people who have already been exposed to machine learning so that they can better understand more complex concepts. Moreover, it is a succinct book on ML, making it excellent for readers having less time on their hands.

7. Pattern Recognition and Machine Learning

Author – Christopher M. Bishop

Pattern Recognition and Machine Learning is an introduction to modern machine learning, data mining, and statistical analysis techniques. The book explains the basic concepts behind the various ML algorithms used in the aforementioned domains. It does so through a series of comprehensive examples that cover topics ranging from linear regression to support vector machines (SVMs).

Whether you are new to these areas or an experienced practitioner, you will benefit greatly from this carefully paced, step-by-step tutorial on some of the most widely used techniques and tools in data analytics.

8. Natural Language Processing with Python

Author – Steven Bird, Ewan Klein, and Edward Loper

Natural Language Processing with Python is a practical, hands-on guide on natural language processing. It covers some of the most fundamental techniques used in NLP today. Written by Steven Bird, Ewan Klein, and Edward Loper, this is a book for people who want to learn natural language processing, not just read about it.

Each chapter builds on what you’ve learned so far, so you can see your skills improving as you work through the chapters of this book. It follows a tutorial approach that will get you up and running quickly so you can apply the various techniques to your own data sets.

Conclusion

To conclude, there is no perfect machine learning book for everyone. Hence, it means the definition of best books for machine learning varies from reader to reader.

Nonetheless, we have listed some of the best books on the subject available right now that you can learn from without getting too much into what’s good and what’s not. But if you feel like we should have included another book, let us know down in the comments.