If you want to learn about machine learning, now is the best time to do so! That doesn’t mean you can’t learn about machine learning in an easy way. To help you, we’ve put together a list of the 20 best books on machine learning.

**The Hundred-Page Machine Learning Book**

Author – Andriy BurkovLatest Edition – FirstPublisher – Andriy BurkovFormat – ebook (Leanpub)/Hardcover/Paperback

Is it possible to talk about a lot of different things about machine learning in just 100 pages? In the Hundred-Page Machine Learning Book written by Andriy Burkov, he tries to do the same. The book on machine learning is written in a way that is easy to understand. Peter Norvig, the Director of Research at Google, and Sujeet Varakhedi, the Head of Engineering at eBay are both fans of the book. If you want to start learning about Machine Learning, this is the best book to start with.

In the future, after reading this book, you will be able to build and understand complex AI systems. You can even start your own ML-based business. The book, on the other hand, is not for people who are completely new to machine learning. If you want something more basic, look somewhere else.

**Programming Collective Intelligence: Building Smart Web 2.0 Applications**

Author – Toby SegaranLatest Edition – FirstPublisher – O’Reilly MediaFormat – Kindle/Paperback

Among the best books to start learning about machine learning, Programming Collective Intelligence by Toby Segaran was written a long time ago, in 2007, when data science and machine learning were not yet the top jobs. In this book, Python is used as the way to get the information to the people who are reading it.

There isn’t a lot of information about machine learning in Programming Collective Intelligence. It’s more of a guide for how to use ml. The book talks about how to make efficient ml algorithms for getting data from apps, how to make programs that can get data from websites, and how to figure out what the data means. Each chapter has exercises that help you improve the efficiency and effectiveness of the algorithms you read about.

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

Author – Drew Conway and John Myles WhiteLatest Edition – FirstPublisher – O’Reilly MediaFormat – Kindle/Paperback

The Machine Learning for Hackers book is for a seasoned programmer who wants to learn more about how to work with data. In this case, the word “hackers” refers to skilled mathematicians. A good choice for people who know R well is this book. Most of the book is about data analysis in R. The book also talks about how to use advanced R in data wrangling.

Perhaps the most important thing about the Machine Learning for Hackers book is that it includes case studies that show how important it is to use machine learning algorithms. Instead of going into more detail about the mathematical theory of machine learning, the book talks about a lot of real-world examples to make learning ml easier and faster.

**Machine Learning**

Author – Tom M. MitchellLatest Edition – FirstPublisher – McGraw Hill EducationFormat – Paperback

Machine Learning by Tom M. Mitchell is a good book for people who want to start learning about machine learning. It gives an in-depth look at machine learning theorems with pseudocode summaries of the algorithms they describe. The Machine Learning book has a lot of examples and case studies to help the reader learn and understand ml algorithms.

If you want to start a career in machine learning, then you need this book. The book on machine learning is a good choice for any machine learning course or program because it has a well-written narrative, a detailed explanation of ml basics, and project-based homework assignments.

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

Author – Trevor Hastie, Robert Tibshirani, and Jerome FriedmanLatest Edition – SecondPublisher – SpringerFormat – Hardcover/Kindle

There are a lot of people who like statistics. If you want to learn about machine learning from a stats point of view, then The Elements of Statistical Learning is the book to read. The machine learning book focuses on mathematical proofs to show how a ml algorithm works. Before you start reading this book, make sure you know at least a little bit about linear algebra.

The concepts in The Elements of Statistical Learning book aren’t easy for people who aren’t very familiar with them. As a result, you might find it hard to understand. There is a book called An Introduction to Statistical Learning if you still want to learn them but don’t know where to start. People who are just starting out will be able to understand it.

**Learning from Data: A Short Course**

Author – Yaser Abu Mostafa, Malik Magdon-Ismail, and Hsuan-Tien LinLatest Edition – FirstPublisher – AMLBookFormat – Hardcover/Kindle

Want to learn more about machine learning in less time? And do you know a lot about engineering math? Learn from Data: A Short Coursebook is a good place to start. Instead of teaching its readers about all of the advanced machine learning concepts, the book helps its readers better understand the complicated machine learning concepts.

The Learning from Data: A Short Coursebook doesn’t use long, complicated explanations. Instead, it gives short, to the point explanations that don’t waste your time. To help you remember what you learned from this book about machine learning, you can also look at the online tutorials from the author, Y.

**Pattern Recognition and Machine Learning**

Author – Christopher M. BishopLatest Edition – SecondPublisher – SpringerFormat – Hardcover/Kindle/Paperback

In this book, Pattern Recognition and Machine Learning was written by Christopher M. Bishop. It’s a great resource for people who want to learn about and use statistical techniques in machine learning and pattern recognition. Having a good grasp of linear algebra and multivariate calculus are important for reading the book on machine learning.

The Pattern Recognition and Machine Learning book has a lot of detailed practice exercises that give you a good overview of statistical pattern recognition techniques. Graphical models are used in a unique way in the book to describe probability distributions. There isn’t a need to have a lot of experience with probability, but it will speed up the learning process.

**Natural Language Processing with Python**

Author – Steven Bird, Ewan Klein, and Edward LoperLatest Edition – FirstPublisher – O’Reilly MediaFormat – Available

In machine learning systems, natural language processing is at the heart of what they do. Python is used to teach you how to use NLTK, a popular set of Python libraries and programs for symbolic and statistical natural language processing for English and NLP in general. This book uses Python to show you how to use the NLTK library and programs.

The Natural Language Processing with Python book shows powerful Python codes that show how NLP works in a clear and precise way. Readers can use well-annotated datasets to look at and deal with unstructured data, linguistic structure in text, and other NLP-related things.

**Bayesian Reasoning and Machine Learning**

Author – David BarberLatest Edition – FirstPublisher – Cambridge University PressFormat – Hardcover/Kindle/Paperback

A book called Bayesian Reasoning and Machine Learning is a must-have for anyone who wants to work in the field of machine learning. The book is a good choice for computer scientists who want to learn ml but don’t know much about calculus and linear algebra.

Bayesian Reasoning and Machine Learning is full of well-explained examples and exercises that show how to do the things that are in the book. This also makes the book good for students who study computer science at both the college and university level. Add-ons and software are included with the machine learning book, as well as extra online resources and a complete package for instructors.

**Understanding Machine Learning**

Author – Shai Shalev-Shwartz and Shai Ben-DavidLatest Edition – FirstPublisher – Cambridge University PressFormat – Hardcover/Kindle/Paperback

The Understanding Machine Learning book is a good way to learn about machine learning in a logical way. The book talks about the basic theories and algorithms of machine learning, as well as mathematical proofs.

There is a lot of information about machine learning that is easy to understand in the machine learning. The Understanding Machine Learning book is for anyone who wants to learn more about machine learning, from computer science students to people who aren’t very knowledgeable about computer science, engineering, math, or statistics.