List of 10 Free Must-Read Books for Machine Learning

10 Free Must-Read eBooks on Machine Learning Basics for Programmers

10 Free Must-Read Machine Learning E-Books For Data Scientists 


Machine learning is a use of Artificial Intelligence that gives a system a capacity to naturally take in and enhance from experiences without being unequivocally modified.

Below I have listed some of my favorite free machine learning/Data science books from where you can download and kick start Machine Learning Basics 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 : Click Here



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.

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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.

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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.

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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.

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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.

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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.

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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 : Click Here


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.

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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.

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Further Reading Free eBooks which might interest you:


Introduction to Machine Learning

Machine Learning

Machine Learning – The Complete Guide

Artificial Intelligence: A Modern Approach

Pattern Recognition and Machine Learning

Machine Learning by Tom M Mitchell

Learning from Data

Neural Networks and Deep Learning

Bayesian Reasoning and Machine Learning

Information Theory, Inference, and Learning Algorithms

Supervised Sequence Labelling with Recurrent Neural Networks

Reinforcement Learning: An Introduction








School of Data Handbook

Data Jujitsu: The Art of Turning Data into Product

The Data Analytics Handbook

Understanding the Chief Data Officer

Think Python: How to Think Like a Computer Scientist

Python Programming

Learn SQL The Hard Way

SQL Tutorial

An Introduction to Data Science


PS: Download all these Free eBooks now and start learning Machine Learning.




6 Easy Steps To Get Started Learning Artificial Intelligence

10 Free Training Courses on Machine Learning and Artificial Intelligence

 A Complete Guide on Getting Started with Deep Learning

Learn TensorFlow and deep learning, without a Ph.D.

15 algorithms machine learning engineers must need to know

7 Best Online courses to Learn Deep Learning

A List of Free AI Software Programs to Download

About Manjunath 77 Articles - Favourite Blog about Artificial Intelligence, Bot- Manjunath


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