10 free Machine Learning ebooks all scientists & AI engineers should read

Machine learning is an application of artificial intelligence that gives a system the ability to learn in real time and improve itself from experiences without being explicitly programmed. As the machine learning industry continues to advance, it’s important to continue learning about it yourself. In this article, we have listed some of the best machine learning books available for free, that you should consider.

Think Stats – Probability and Statistics for Programmers

Author: Allan B. Downey

‘Think Stats’ is an introductory book to statistics and probability for people with a basic background in Python programming. It’s based on a Python library for probability distributions (PMFs and CDFs). To make things easier for the reader, most of the exercises have short programs. The book also includes a case study using data from the National Institutes of Health.

One of the stand-out features of this book is it covers the basics of Bayesian statistics as well, a very important branch for any aspiring data scientist.

The Art of Data Science

Authors: Roger D. Peng and Elizabeth Matsui

Focusing on the analysis and distillation of data, this book by offers a bird’s eye view for practitioners as well as managers in data science.

This book describes the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses. It is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.

Mining Of Massive Datasets

Authors: Jure Leskovec, Anand Rajaraman, and Jeffrey David Ullman

Huge data sets know as “Big Data” can be enormous in size and overwhelming to work with., How should one sift it efficiently for accurate and relevant information? The book, based on a Stanford Computer Science course, is designed for Data Analysis enthusiasts, who may not hold a formal qualification in the subject.

Big-data is transforming the world. Here you will learn data mining and machine learning techniques to process large datasets and extract valuable knowledge from them.

An Introduction to Statistical Learning

Authors: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

One of the most popular entries in this list, it’s an introduction to data science through machine learning. This book gives clear guidance on how to implement statistical and machine learning methods for newcomers to the field. It’s filled with practical real-world examples of where and how algorithms work.

For those with an inclination towards R programming, this book contains practical examples in R. In case you’re not a programmer, don’t let that put you off. This book is a gem.

Machine Learning Yearning

Author: Andrew NG

Author Andrew Ng states that the book’s objective is to “teach one how to make the numerous decisions needed with organising a machine learning project.”

Historically, the only way to learn how to make these “strategy” decisions has been a multi-year apprenticeship in a graduate program or company. “I am writing a book to help you quickly gain this skill, so that you can become better at building AI systems,” says the author.

Deep Learning

Authors: Ian Goodfellow, Yoshua Bengio and Aaron Courville

This is one of the most comprehensive books written in the deep learning field. Concepts like Monte Carlo Methods, Recurrent and Recursive Nets, Autoencoders and Deep Generative Models (among others) are covered in detail.

Data Jujitsu

Author: DJ Patil

Former US Chief Data Scientist DJ Patil uses the technique of using the problem’s “weight” against itself to find a solution.

Learn more about the problems before starting on the solutions—and use the findings to solve them, or determine whether the problems are worth solving at all.

Understanding Machine Learning

Authors: Shai Shalev-Shwartz and Shai Ben-David

For mathematics- savvy people, this is one of the most recommended book for understanding the magic behind Machine Learning. It looks at the fundamental theories of machine learning and the mathematical derivations that transform these concepts into practical algorithms. Following that, it covers a list of ML algorithms, including, stochastic gradient descent, neural networks, and structured output learning.

Bayesian Methods for Hackers

Author: Cameron Davidson-Pilon

This book introduces you to the Bayesian methods and probabilistic programming from a computation point of view. The book geared towards readers with a loose grip on mathematics.

The Data Science Handbook

Authors: Carl Shan, William Chen, Henry Wang, and Max Song

This book is a compilation of interviews with 25 data scientists, where they share their insights, stories, and advice. Even though the book is not a technical guide to data science, the personal stories of noted personalities guide the reader to figuring out their own plan of action.

If you’re an aspiring data scientist, this book will provide a great view of the landscape of this new career path. If you’re leading data science teams, this book can shed light on how to work with and develop data scientists.

As the machine learning technologies continue to advance, it is important for data scientists and engineers to keep up. These free e-books provide the best insights into the ever-changing industry and will help any member of these fields with the information they need to stay up-to-date.

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Amanda Peterson

Amanda Peterson is a contributor to Enlightened Digital and software engineer from New York City. When she’s not trying to find the best record store in the city, you can find her curling up to watch some Netflix with her Puggle, Hendrix.