# 15 Machine Learning Books You Need to Read!

If you are interested in coding, machine learning and computer languages… if terms such as “convex optimization”, or “neural networks” mystify and excite you, you know you have come to the right place! We have shortlisted 15 books you must read to cultivate your love for Artificial Intelligence, which will help you unravel the mysteries of the computer universe!

### 1. Automate the Boring Stuff with Python ~ Al Sweigart

In Automate the Boring Stuff with Python, you’ll learn how to use Python to write programs that do in minutes what would take you hours to do by hand-no prior programming experience required. Once you’ve mastered the basics of programming, you’ll create Python programs that effortlessly perform useful and impressive feats of automation

URL: https://automatetheboringstuff.com/

### 2. Think Bayes ~ Allen B Downey

Think Bayes is an introduction to Bayesian statistics using computational methods. Most books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops.

URL: http://greenteapress.com/wp/think-bayes/

### 3. Neural Networks and Deep Learning ~ Michael Nielson

Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you many of the core concepts behind neural networks and deep learning.

URL: http://neuralnetworksanddeeplearning.com/

### 4. Python data science handbook ~ Jake VanderPlas

This book provides information on IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools., unlike most books which provide information only on one tool at a time. This is the must-have reference for scientific computing in Python.

URL: https://github.com/jakevdp/PythonDataScienceHandbook

### 5. Real world machine learning ~ Henrick Brink

Real-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you’ll build skills in data acquisition and modeling, classification, and regression.

URL: https://www.manning.com/books/real-world-machine-learning

### 6. Artificial Intelligence: A Modern Approach ~ Peter Norvig and Stuart Russell

This book is the mother of all books when it comes to learning and studying machine language. It is not only a high university level textbook but also provides a deep understanding of machines and the languages you can use.

### 7. The Hundred Page Machine Learning Book ~ Andriy Burkov

This book has been endorsed by Peter Norwig, Gareth James and Aurelien Geron. This book contains information for supervised and unsupervised learning, supporting vector machines, neural networks, ensemble methods, gradient, descent, cluster analysis and dimensionality reduction, auto encoders and transfer learning, feature engineering and hyper parameter tuning, all in a hundred pages.

### 8. Machine Learning ~ Tom Mitchell

This textbook provides a single source introduction to the primary approaches to machine learning. It is intended for advanced undergraduate and graduate students, as well as for developers and researchers in the field. No prior background in artificial intelligence or statistics is assumed. Several key algorithms, example date sets and project- oriented homework assignments discussed in the book are accessible through the World Wide Web.

URL: http://www.cs.cmu.edu/~tom/mlbook.html

### 9. The Elements of Statistical Learning ~ Trevor Hastie

This book was written by Trevor Hastie, Robert Tibshirani and Jerome Friedman. This book aptly explains various machine learning algorithms mathematically from a statistical perspective. It provides a powerful world created by statistics and machine learning. This books lays emphasis on mathematical derivations to define the underlying logic behind an algorithm. Keep in mind that you need to have a rudimentary understanding of linear algebra before picking this up.

URL: https://web.stanford.edu/~hastie/ElemStatLearn/

### 10. Artificial Intelligence for Humans ~ Jeff Heaton

This book comes with 3 separate volumes. It teaches basic artificial intelligence algorithms such as dimensionality, distance metrics, clustering, error calculation, hill climbing, and linear regression. It explains these algorithms using interesting examples and cases.

URL: https://www.heatonresearch.com/aifh/

### 11. Programming Collective Intelligence ~ Toby Segaran

*Programming
Collective Intelligence** *takes you
into the world of machine learning and statistics, and explains how to draw
conclusions about user experience, marketing, personal tastes, and human
behaviour in general — all from information that you and others collect every
day. Each algorithm is described clearly and concisely with code that can
immediately be used on your web site, blog, Wiki, or specialized application.

URL: http://shop.oreilly.com/product/9780596529321.do

### 12. Convex Optimization ~ Stephen Boyd

This book is about convex optimization, a special class of mathematical optimization problems, which includes least-squares and linear programming problems. It is well known that least-squares and linear programming problems have a fairly complete theory, arise in a variety of applications, and can be solved numerically very efficiently. The basic point of this book is that the same can be said for the larger class of convex optimization problems.

URL: http://stanford.edu/~boyd/cvxbook/

### 13. Foundations of Data Science ~ Avrin Blum, John Hopcraft, Ravindran Kumar

This book is free and available for download. It can be useful for academic work or in business. It covers topics such as machine Learning, Massive Data, Clustering and many more. Attached is the free pdf link of the book.

URL: https://www.cs.cornell.edu/jeh/book.pdf

### 14. Deep Learning Textbook ~ Ian Goodfellow

The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free. Attached is the free pdf link.

URL: http://www.deeplearningbook.org/front_matter.pdf

### 15. Machine Learning Yearning ~ Andrew Ng

This book is focused not on teaching you ML algorithms, but on how to make ML algorithms work. Some technical AI classes will give you a hammer; this book teaches you how to use the hammer. If you aspire to be a technical leader in AI and want to learn how to set direction for your team, this book will help. You can sign up for a chapter by chapter free draft copy mailing.

URL: https://www.mlyearning.org/

Here’s to your next glorious reading list!