The best machine learning books 📚

Browse the best books on machine learning as recommended by authors, experts, and creators. Along with notes on why they recommend those books.

Coming Fall 2022: The ability to sort this list by genre (signup here to follow our story as we build a better way to discover books).

Book cover of Advances in Financial Machine Learning

Advances in Financial Machine Learning

By Marcos Lopez de Prado

Why this book?

By now, you may notice that I like to recommend textbooks. I use this bestseller for my course in Financial Machine Learning at Northwestern University, but really, nobody interested in financial machine learning hasn’t read this book. The topics are highly relevant to every investor or trader – I read it at least 5 times to digest every nugget and have put them to very productive use in my trading as well as in my fintech firm predictnow.ai. It covers basic techniques such as random forest to advanced techniques such as Hierarchical Risk Parity, which is a big improvement over…

From the list:

The best quantitative trading books for beginners

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

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

By Trevor Hastie, Robert Tibshirani, Jerome Friedman

Why this book?

This book might as well be called Introduction to machine learning, and it is probably one of the only books truly deserving of the title. Did you know neural networks have been used for decades to scan checks at the bank? They are called Boltzman Machine. Have you ever heard of how decision trees were used in old-school data mining? You could only get them from proprietary software packages from the early 2000s.

In quant trading, you will constantly face compute power constraints, so it is invaluable to understand the mathematical foundations of the most old-school machine learning methods…

From the list:

The best mathematics books for quant finance

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Book cover of Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems

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

By Géron Aurélien

Why this book?

There are three types of machine learning books — books written for people who want to become machine learning engineers, books written for people who want to become machine learning researchers, and books written for business executives. Reading a book written for researchers or executives can be a frustrating experience if you are a software engineer, social scientist, or mechanical engineer who wants to learn machine learning and get an ML job in the industry.

If you are a coder who wants to become an ML engineer, you have got to learn machine learning concepts, but you…

From the list:

The best books if you want to become a machine learning engineer

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Book cover of Deep Learning with Python

Deep Learning with Python

By Francois Chollet

Why this book?

This is a fantastic book to get you started. It is written by the author of a leading deep learning framework Keras, which makes even Tensorflow very easy to use. Chollet is a true leader of the deep learning craft and the Manning team always does an excellent job of forcing authors to make the subject matter accessible. Highly recommended!

From the list:

The best books about applied deep learning

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Book cover of Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems

Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems

By Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta

Why this book?

This recommendation is a bit of a cheat — I’m not recommending this exact book, but one of the books in the series that this book is part of.

Once you have the first two books under your belt, you’ll know how to solve ML problems. But you will keep reinventing the wheel. What you need next is a book on common “ML tricks” — best practices and common techniques when doing ML in production.

The problem is that these tricks are specific to the type of data that you will be processing. If you are going to be processing…

From the list:

The best books if you want to become a machine learning engineer

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Book cover of Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD

Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD

By Jeremy Howard, Sylvain Gugger

Why this book?

Jeremy Howard is the lead author and has always been a world-class educator. This book is based on his fast.ai course, which has managed to splice all rigor, simplicity, and cutting edge techniques into one course. It also uses its custom fast.ai framework built on PyTorch, which is the dominant language for researchers. This book is very practically oriented and gets you off the ground very quickly with your own projects!

From the list:

The best books about applied deep learning

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