5 Free Machine Learning E-Book Resources

In this post, I will share five free machine learning e-book resources with you. It will be a short and simple post. I will introduce you to each resource by giving some basic information about them. I tried to find resources with online access so that anyone from anywhere can have free access to them. Hoping that you will find them helpful. Reading a programming book can be a bit challenging, but instead of reading the whole book, I usually check their index and read the parts find interesting or need to learn at that moment. Let’s get started!

One – Python Data Science Handbook

This is will a book resource but you can still find the content on the webpage. It is book written by Jake VanderPlas. Jake VanderPlas was formerly both the Director of Open Software and the Director of Research in Physical Sciences for the eScience Institute; he now works at Google. In this book, you can also find helpful content about Numpy, Pandas, and Matplotlib libraries.


Two – Python 101

Another helpful book resource, written by Michael Driscoll. This is a great resource, if you are just getting started with Python. It is also a great read to refresh your Python knowledge. This book will help you learn how to program with Python 3 from beginning to end. Python 101 starts off with the fundamentals of Python and then moves on to Python’s standard libraries. 


Three – Machine Learning and Big Data

This resource a little more academic than others. The implementation of machine learning and big data can be found. The projects are mostly solved using Python, C++, Java and Scala. This will be good read if you are interested to learn more about Big Data.


Four – Mathematics for Machine Learning

This book focuses more on the mathematics behind machine learning. If you are interested to improve your machine learning knowledge, you should know how it works. When you know how something works, you have more power to change and try new things. The book is divided into two sections:

  • Mathematical foundations
  • Example machine learning algorithms that use the mathematical foundations


Five – The Hundred Page Machine Learning Book

This book covers most of the new trends in machine learning. Written by Andriy. He is a dad of two and a machine learning expert based in Quebec City, Canada. Nine years ago, he got a Ph.D. in Artificial Intelligence, and for the last seven years, he has been leading a team of machine learning developers at Gartner. 

This book is like one cup of smoothie with all your favorite fruits. Healthy and concise 🙂

In one paragraph the topics covered in this book can be listed as: Supervised and unsupervised learning, support vector machines, neural networks, ensemble methods, gradient descent, cluster analysis and dimensionality reduction, auto-encoders and transfer learning, feature engineering and hyper-parameter tuning! Math, intuition, illustrations, all in just a hundred pages!



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