11 Free Books On Machine Learning & Data Science That’ll Give You A Major Edge Over Your Competitors

It’s as good a time as any to keep yourself updated — especially for those who are in the ever-changing technology field. If you’re interested in, or working as a professional in Data Science, Machine Learning and allied fields, we’ve compiled a list of top 11 books that are available free that you must catch up on gloomy rainy days.

1) The Art of Data Science

Focusing on analysis and distillation of data, the book by Roger D Peng and Elizabeth Matsui 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, and this book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.

You can download the book here:

2) Understanding Machine Learning: From Theory to Algorithms

This book by Shai Shalev-Shwartz and Shai Ben-David, introduces machine learning and the algorithmic paradigms it offers, in a principled manner.

The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks.

You can download the book here:



3) Think Stats — Probability and Statistics for Programmers

This book written by Allen B Downey and published by O’Reilly Media, is an introduction to Probability and Statistics for Python programmers.

Most introductory books don’t cover Bayesian statistics, but Think Stats is based on the idea that Bayesian methods are too important to postpone. By taking advantage of the PMF and CDF libraries, it is possible for beginners to learn the concepts and solve challenging problems.

You can download the book here:


4) The Data Science Handbook

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.

You can download the book here:


5) Machine Learning Yearning

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.

You can download the book here:


6) Data Driven

In this O’Reilly publication, former US Chief Data Scientist DJ Patil and scholar Hilary Mason outline the steps a person needs to take if they want their company to be truly data-driven.

Succeeding with data isn’t just a matter of putting Hadoop in your machine room, or hiring some physicists with crazy math skills. It requires you to develop a data culture that involves people throughout the organization

You can download the free Kindle edition on Amazon.



7) Mining Of Massive Data

Big Data is enormous in size, but 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.

You can download the book here:



8) A Brief Introduction to Neural Networks

From history of Neural Networks, to its training, author D Kriesel systematically explains the subject in this book.

Neural networks are a bio-inspired mechanism of data processing, that enables computers to learn technically similar to a brain and even generalize once solutions to enough problem instances are tought.

You can download the book here:




9) Data Jujitsu

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.

You can download the book here:




10) Building Data Science Teams

The book discusses the importance of assembling a strong and innovative data team. The skills required, perspectives to look forward to, and tools used to processes data are discussed in the book.

Topics include such as “What it means to be data driven,” the unique roles of data scientists, the four essential qualities of data scientists and DJ Patil’s first-hand experience building the LinkedIn data science team are also included in the book.

You can download the free Kindle edition from Amazon here:


11) Planning Algorithms

The book covers a plethora of planning required to run and execute various programmes for Artificial Intelligence, Machine Learning and Robotics.

You can download the book here:

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Prajakta Hebbar
Prajakta is a Writer/Editor/Social Media diva. Lover of all that is 'quaint', her favourite things include dogs, Starbucks, butter popcorn, Jane Austen novels and neo-noir films. She has previously worked for HuffPost, CNN IBN, The Indian Express and Bose.

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