Machine Learning Bookshelf

Below, I share a list of interesting machine learning books I refer to for my work. Most of these books have a free version available on their website and can be ordered from Amazon. I have included links to relevant HN discussions, as it is how I found out about these books in most of the cases.

Have a great read,

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An Introduction to Statistical Learning Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
Official website

HN: Ask HN: How to get started with machine learning? (950)

This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more.

The Elements of Statistical Learning Trevor Hastie, Robert Tibshirani, Jerome Friedman
Official website

HN: Ask HN: How to get started with machine learning? (950)

This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry.

Python Machine Learning Sebastian Raschka
Official website

HN: Python Machine Learning (128)

If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.

Taming Text Grant S. Ingersoll, Thomas S. Morton, Drew Farris
Official website

HN: Ask HN: How Can I Get into NLP (Natural Language Processing)? (297)

Taming Text, winner of the 2013 Jolt Awards for Productivity, is a hands-on, example-driven guide to working with unstructured text in the context of real-world applications.

Advanced Analytics with Spark Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills
Official website

HN: Apache Spark Scale: A 60 TB+ production use case (254)

In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example.

Resonate: Present Visual Stories That Transform Audiences Nancy Duarte
Official website

HN: n/a

Resonate helps you make a strong connection with your audience and lead them to purposeful action. The author’s approach is simple: building a presentation today is a bit like writing a documentary. Using this approach, you’ll convey your content with passion, persuasion, and impact.

Deep Learning Ian Goodfellow, Yoshua Bengio, Aaron Courville
Official website

HN: Free “Deep Learning” Textbook by Goodfellow and Bengio Now Finished (603)

“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” – Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX

Reinforcement Learning: An Introduction Richard S. Sutton , Andrew G. Barto
Official website

HN: New Draft of “Reinforcement Learning: An Introduction, Second Edition” (170)

In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field’s intellectual foundations to the most recent developments and applications.