Top 30 Machine Learning Books You Should Read

Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to “learn” from data, without being explicitly programmed.

1.

Pattern Recognition and Machine Learning

Christopher M. Bishop | 2006 | amazon.com

2.

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

Trevor Hastie | 2001 | amazon.com

3.

Machine Learning: A Probabilistic Perspective

Kevin P. Murphy | 2012 | amazon.com

4.

Deep Learning

Ian Goodfellow | | amazon.com

5.

Machine Learning

Tom M. Mitchell | 1986 | amazon.com

6.

An Introduction to Statistical Learning: With Applications in R

Gareth James | 2013 | amazon.com

7.

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

Pedro Domingos | 2015 | amazon.com

8.

Hands-On Machine Learning with Scikit-Learn and TensorFlow

Aurélien Géron | 2017 | amazon.com

9.

Information Theory, Inference and Learning Algorithms

David J.C. MacKay | 2002 | amazon.com

10.

Python Machine Learning

Sebastian Raschka | 2015 | amazon.com

11.

Bayesian Reasoning and Machine Learning

David Barber | 2012 | amazon.com

12.

Programming Collective Intelligence: Building Smart Web 2.0 Applications

Toby Segaran | 2002 | amazon.com

13.

Learning From Data: A Short Course

Yaser S. Abu-Mostafa | 2012 | amazon.com

14.

Artificial Intelligence: A Modern Approach

Peter Norvig | 1994 | amazon.com

15.

Probabilistic Graphical Models: Principles and Techniques

Daphne Koller | 2009 | amazon.com

16.

Data Mining: Practical Machine Learning Tools and Techniques

Ian H. Witten | 1999 | amazon.com

17.

Machine Learning for Hackers

Drew Conway | 2012 | amazon.com

18.

Machine Learning in Action

Peter Harrington | 2011 | amazon.com

19.

Mining of Massive Datasets

Anand Rajaraman | 2011 | amazon.com

20.

Neural Networks and Deep Learning

Michael Nielsen | 2013 | amazon.com

21.

Reinforcement Learning: An Introduction

Richard S. Sutton | 1998 | amazon.com

22.

Understanding Machine Learning: From Theory to Algorithms

Shai Shalev-Shwartz | 2014 | amazon.com

23.

Building Machine Learning Systems with Python

Willi Richert | 2013 | amazon.com

24.

Pattern Classification

David G. Stork | 1973 | amazon.com

25.

Gaussian Processes for Machine Learning

Carl Edward Rasmussen | 2005 | amazon.com

26.

Introduction to Machine Learning

Ethem Alpaydin | 2004 | amazon.com

27.

Data Science from Scratch: First Principles with Python

Joel Grus | 2015 | amazon.com

28.

Applied Predictive Modeling

Max Kuhn | 2013 | amazon.com

29.

Make Your Own Neural Network

Tariq Rashid | | amazon.com

30.

Bayesian Data Analysis

Andrew Gelman | 1995 | amazon.com