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30 Free Machine learning Books PDF

Hey all, Welcome to Insane. Today, We will go through some of the best free machine learning books pdf available on the internet. You can read this books on authors website or download PDF for free from the below given links. If you find this post useful, please share it with your friends as well as with others.

Let’s go inside this list…

Best Machine Learning Books PDF Are As Follows

Best Free Machine Learning Books

1. Understanding Machine Learning: From Theory to Algorithms PDF

Book by Shai Ben-David and Shai Shalev-Shwartz

Aim of this Textbook: To provide Introduction to ML & Algorithmic paradigms

Download Link: Understanding Machine Learning: From Theory to Algorithms

2. Bayesian Reasoning and Machine Learning PDF

Book by David Barber

Who this book is for: Final Year Undergraduates & Masters Students

Download Link: Bayesian Reasoning & Machine Learning

3. Neural Networks and Deep Learning PDF

Book by Michael Nielsen

What you will Learn: You’ll learn the concepts behind neural networks and deep learning.

Download Link: Neural Networks and Deep Learning

4. Artificial Intelligence: A Modern Approach PDF

Book by Stuart Russell

What this Book Provides: Introduction to the theory and practice of artificial intelligence

Download Link: Artificial Intelligence: A Modern Approach

5. Gaussian Processes for Machine Learning

Book by C. E. Rasmussen & C. K. I. Williams

What this Book Provides: A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.

Download Link: Gaussian Processes for Machine Learning

6. The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Book by Trevor Hastie, Robert Tibshirani, & Jerome Friedman

Some of the topics inside this Book: Overview of Supervised Learning, Linear Methods for Regression, Basis Expansions and Regularization, Neural Networks, Support Vector Machines and Flexible Discriminants, Unsupervised Learning, Random Forests, Ensemble Learning, and more

Download Link: The Elements of Statistical Learning: Data Mining, Inference, and Prediction

7. Natural Language Processing with Python PDF

Book by Steven Bird

What this Book offers: This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text & email filtering to automatic summarization & translation. With it, you’ll study how to write Python programs that work with long collections of unstructured text.

Download Link: Natural Language Processing with Python

8. R Programming for Data Science PDF

Book by Roger D. Peng

About this Book: This book is about the fundamentals of R programming. You will get incited with the basics of the R programming language, learn how to write functions, how to manipulate datasets, & how to debug and optimize code.

Download link: R Programming for Data Science

9. Reinforcement Learning: An Introduction PDF

Book by Richard S. Sutton & Andrew G. Barto

Focus of this Book: This book focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes.

Download Link: Reinforcement Learning: An Introduction

10. Probabilistic Programming & Bayesian Methods for Hackers

Book by Cam Davidson-Pilon

Book is Designed for: This book is designed as an introduction to Bayesian inference from a computational understanding-first, and mathematics-second, point of view. The book assumes no awareness of Bayesian inference neither probabilistic programming.

Download Link: Probabilistic Programming & Bayesian Methods for Hackers

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11. Algorithms for Reinforcement Learning PDF

Book by Csaba Szepesvari

What this Book Covers: This book gives a very quick but still thorough introduction to reinforcement learning, and includes algorithms for quite a few methods.

Download Link: Algorithms for Reinforcement Learning

12. Information Theory, Inference, and Learning Algorithms

Book by David J.C. MacKay

Little bit about this Book: Essential reading for students of electrical engineering and computer science; also a great heads-up for mathematics students concerning the subtlety of many commonsense questions.”

Download Ebook: Information Theory, Inference, and Learning Algorithms

13. Computer Vision: Algorithms and Applications

Book by Richard Szeliski

Author says, This book also reflects my years’ experience doing computer vision research in corporate research labs […] I have mostly focused on problems and solution techniques (algorithms) that have practical real-world applications and that work well in practice. Thus, this book has more emphasis on basic techniques that work under real-world conditions and less on more esoteric mathematics that has intrinsic elegance but less practical applicability.

Download Link: Computer Vision: Algorithms and Applications

14. Programming Computer Vision with Python

Book by Jan Erik Solem

Download Link: Programming Computer Vision with Python

15. Data Mining: Practical Machine Learning Tools and Techniques PDF

Book by Ian H. Witten & Eibe Frank

Download Link: Data Mining: Practical Machine Learning Tools and Techniques

16. Mining of Massive Datasets

Book by Jure Leskovec, Anand Rajaraman, & Jeff Ullman

Focus of this Book: This book focuses on practical algorithms used to solve key problems in data mining, with problems suitable for students from the intermediate level students and beyond.

Download Link: Mining of Massive Datasets

18. An Introduction to Data Science PDF

Book by Jeffrey Stanton

Book Description: This book is an easy-to-read, gentle introduction for people with a wide range of backgrounds into the world of data science

Download Link: An Introduction to Data Science

19. Data-Intensive Text Processing with MapReduce PDF

Book by Jimmy Lin & Chris Dyer

Focus of this Book: This book focuses on MapReduce algorithm design, with an accent on text processing algorithms common in NLP, information retrieval, & machine learning.

Download Link: Data-Intensive Text Processing with MapReduce

20. The Data Science Handbook PDF

Book by by Carl Shan,‎ William Chen,‎ Henry Wang,‎ Max Song

What’s Inside this Book: The Data Science Handbook is a compilation of in-depth interviews with professional data scientists, where they share their knowledge, stories, & advice.

Download Link: The Data Science Handbook

21. The Art of Data Science PDF

Book by Roger D. Peng & Elizabeth Matsui

Book Description: This book describes, simply and in general terms, the process of analyzing data.

Download Link: The Art of Data Science

22. Invent Computer Games with Python PDF

Book by Al Sweigart

What you’ll Learn: Each chapter gives you the complete source code for a new game and teaches the programming concepts from these examples.

Download Link: Invent Computer Games with Python

23. Learn Python, Break Python PDF

Book by Scott Grant

Download Link: Learn Python, Break Python

24. Think Stats: Exploratory Data Analysis in Python PDF

Book by Allen B. Downey

Download Link: Think Stats: Exploratory Data Analysis in Python

25. An Introduction to Statistical Learning with Applications in R PDF

Book by Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani

Some of the topics inside this Book: Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, and much more.

Download link: An Introduction to Statistical Learning with Applications in R

26. The R Inferno PDF

Book by Patrick Burns

What this Book is About: A book about trouble spots, oddities, traps, glitches in R

Download Link: The R Inferno Ebook

27. Automate the Boring Stuff with Python: Practical Programming for Total Beginners PDF

Book by Al Sweigart

What you’ll Learn: You’ll learn how to use Python to write programs that do in minutes what would take your ampere-hours to create by hand with no prior programming experience needed. If you want the best books for python for machine learning then this one is for you.

Download Link: Automate the Boring Stuff with Python: Practical Programming for Total Beginners

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