Free Data Science eBooks - March 2017
Every month we scour the internet seeking out free eBooks to help you on your educational journey, and this month has been no different.
I hope these books prove to be a valuable resource to you and that you will visit regularly (and invite your friends too).
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Disclosure: as well as links to the free ebooks we may also include links to non-free versions of the same books, and we may earn an affiliate commission for purchases you make when using those links
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This month, we have 3 books about Machine Learning. They're all FREE, so get cracking...
Edited by Abdelhamid Mellouk and Abdennacer Chebira
Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behaviour.
Machine Learning addresses more specifically the ability to improve automatically through experience.
NOTE: This ebook is not currently available to purchase at Amazon, but a similar book you might like to check out is Machine Learning For Absolute Beginners: A Plain English Introduction ← *big fat scary affiliate link!*
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by Shai Ben-David and Shai Shalev-Shwartz
Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way.
The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.
Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.
NOTE: This ebook is currently listed as free for only pdf versions. If you would rather have an alternative version (including hardback or paperback) you can get a paid copy from Amazon ← *big fat scary affiliate link!*
by D. Kriesel
The purpose of this book is to help you master the core concepts of neural networks, including modern techniques for deep learning.
After working through the book you will have written code that uses neural networks and deep learning to solve complex pattern recognition problems.
And you will have a foundation to use neural networks and deep learning to attack problems of your own devising.
NOTE: This ebook is not currently available to purchase at Amazon, but a similar book you might like to check out is An Introduction to Neural Networks ← *big fat scary affiliate link!*
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