Below I have listed some of my favorite free machine learning/Data science ebooks from where you can download and kick start Machine Learning Basics/Statistics for developers to become good at building AI systems quickly.
1. Machine Learning Yearning – By Andrew Ng
AI, Machine Learning and Deep Learning are changing various enterprises. This book rapidly pick up with the goal that you can turn out to be better at building AI frameworks.
Where to Down load : http://www.mlyearning.org/
2. Understanding Machine Learning: From Theory to Algorithms – By Shai Shalev-Shwartz and Shai Ben-David
Machine learning is one of the quickest developing ranges of software engineering, with expansive applications. This book presents machine learning, and the algorithmic standards it offers, principledly. The book gives a hypothetical record of the basics basic machine learning and the numerical deductions that change these standards into useful calculations. this book covers critical algorithmic standards including stochastic slope plunge, neural systems, and organized yield learning; and developing hypothetical ideas.
Where to Down load : http://www.cs.huji.ac.il/%7Eshais/UnderstandingMachineLearning/
3. Think Stats: Probability and Statistics for Programmers – By Allen B. Downey
Think Stats is a prologue to Probability and Statistics for Python developers.
Think Stats accentuates basic strategies you can use to investigate genuine informational collections and answer intriguing inquiries.
Where to Down load : http://www.greenteapress.com/thinkstats/
4. Probabilistic Programming and Bayesian Methods for Hackers – By Cam Davidson-Pilon
An introduction to Bayesian strategies and probabilistic programming from a calculation to start with, arithmetic second perspective.
The Bayesian strategy is the normal way to deal with inference, yet it is avoided perusers behind sections of moderate, numerical examination. The regular content on Bayesian surmising includes a few sections on likelihood hypothesis, then enters what Bayesian derivation is.
5. The Elements of Statistical Learning – By Trevor Hastie, Robert Tibshirani and Jerome Friedman
The book’s scope is expansive, from administered learning (expectation) to unsupervised learning. The numerous points incorporate neural systems, bolster vector machines, characterization trees and boosting- – the primary extensive treatment of this theme in any book.
Where to Down load : http://statweb.stanford.edu/%7Etibs/ElemStatLearn/printings/ESLII_print10.pdf
6. Foundations of Data Science – By Avrim Blum, John Hopcroft, and Ravindran Kannan
This book to cover the hypothesis prone to be helpful in the following 40 years, similarly as a comprehension of automata hypothesis, calculations, and related themes gave understudies favorable position over the most recent 40 years.
Where to Down load : https://www.cs.cornell.edu/jeh/book.pdf
7. An Introduction to Statistical Learning with Applications in R – By Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
The book contains various R labs with itemized clarifications on the most proficient method to actualize the different strategies, all things considered, settings, and ought to be an important asset for a rehearsing information researcher.
Where to Down load : http://www-bcf.usc.edu/%7Egareth/ISL/
8. A Programmer’s Guide to Data Mining: The Ancient Art of the Numerati – By Ron Zacharski
The reading material is laid out as a progression of little strides that expand on each other until, when you finish the book, you have established the framework for understanding information mining systems.
Where to Down load : http://guidetodatamining.com/
9. Deep Learning – By Ian Goodfellow, Yoshua Bengio and Aaron Courville
The Deep Learning course reading is an asset proposed to help understudies and professionals enter the field of machine learning by and large and profound learning specifically. The online adaptation of the book is presently total and will stay accessible online for nothing.
Where to Down load : http://www.deeplearningbook.org/
10. Mining of Massive Datasets – By Jure Leskovec, Anand Rajaraman and Jeff Ullman
The book is outlined at the undergrad software engineering level to bolster further investigations, the majority of the parts are supplemented with further perusing references.
Where to Down load : http://mmds.org/
Further Reading Free eBooks which might interest you:
Introduction to Machine Learning
Amnon Shashua, 2008
Abdelhamid Mellouk & Abdennacer Chebira
School of Data Handbook
School of Data, 2015
Data Jujitsu: The Art of Turning Data into Product
DJ Patil, 2012
The Data Analytics Handbook
Brian Liou, Tristan Tao, & Declan Shener, 2015
Understanding the Chief Data Officer
Julie Steele, 2015
Think Python: How to Think Like a Computer Scientist
Allen Downey, 2012
Learn SQL The Hard Way
Zed. A. Shaw, 2010
An Introduction to Data Science
Jeffrey Stanton, 2013
PS: Download all these Free eBooks now and start learning Machine Learning.