Hi Dev Community. In this post, You'll see some of the best free Machine Learning, Deep Learning, Python, & Data Science Books. You can read this book on authors website or download PDF for free.

Note: All the eBooks are open source. If you still have any doubts on sources, please comment below

1. Machine Learning for Humans

• Authors: Vishal Maini and Samer Sabri

• Who should read this?

a. Technical people who want to get up to speed on machine learning quickly

b. Non-technical people who want a primer on machine learning and are willing to engage with technical concepts

c. Anyone who is curious about how machines think

This guide is intended to be accessible to anyone. Basic concepts in probability, statistics, programming, linear algebra, and calculus will be discussed, but it isn’t necessary to have prior knowledge of them to gain value from this series.

In short, this book contains simple, plain-English explanations accompanied by math, code, and real-world examples.

Download link: Machine Learning for Humans PDF

2. Approaching (Almost) Any Machine Learning Problem

• Author: Abhisek Thakur

• About this Special eBook: This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book doesn't explain the algorithms but is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is not for you if you are looking for pure basics. The book is for you if you are looking for guidance on approaching machine learning problems. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along.

Download link: Approaching (Almost) Any Machine Learning Problem PDF

3. Machine Learning Engineering

• Author: Andriy Burkov

• What experts says about this book: The most comprehensive book on the engineering aspects of building reliable AI systems.

a. "If you intend to use machine learning to solve business problems at scale, I'm delighted you got your hands on this book." - Cassie Kozyrkov, Chief Decision Scientist at Google

b. "Foundational work about the reality of building machine learning models in production." - Karolis Urbonas, Head of Machine Learning and Science at Amazon.

Read here: Machine Learning Engineering eBook

4. 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 PDF

5. 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 PDF

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

• 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: Elements of Statistical Learning PDF

7. Natural Language Processing with Python

• 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.

Read Here: Natural Language Processing with Python eBook

8. Reinforcement Learning: An Introduction

• 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 PDF

9. Computer Vision: Algorithms and Applications

• Book by Richard Szeliski

• This book also reflects my 20 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

10. Mathematics for Machine Learning

• Author: A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth

• About This Special eBook: If you ever need a place to start learning about the maths behind machine learning, then this a highly recommended book. This book provides great coverage of all the basic mathematical concepts for machine learning.

Download link: Mathematics for Machine Learning PDF

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11. Pattern Recognition and Machine Learning eBook

• Author: Christopher M. Bishop

• About This Special eBook: This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Download link: Pattern Recognition and Machine Learning PDF

12. Think Stats: Exploratory Data Analysis in Python

• Book by Allen B. Downey

• About this eBook: If you know how to program, you have the skills to turn data into knowledge using the tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.

Download link: Think Stats: Exploratory Data Analysis in Python PDF

13. An Introduction to Statistical Learning with Applications in R

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

• Some of the topics inside this Book: T0opics 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 PDF

14. Python Data Science Handbook

• Author: Jake VanderPlas

• What's Special about this eBook:

With this handbook, you’ll learn how to use:

1. Python and Jupyter: provide computational environments for data scientists using Python

2. NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python

3. Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python

4. Matplotlib: includes capabilities for a flexible range of data visualizations in Python

5. Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

Read Here: Python Data Science Handbook eBook

15. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

• Author: Garrett Grolemund and Hadley Wickham

• What's Special about this eBook: Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.

Read Here: R for Data Science eBook

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16. Text Mining with R: A Tidy Approach

• Author: David Robinson and Julia Silge

• What's Special about this eBook: With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective.

The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You’ll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media.

Read Here: Text Mining with
R eBook

17. Think Python: How to Think Like a Computer Scientist

• Author: Allen B. Downey

• What's Special about this eBook:

1. Start with the basics, including language syntax and semantics
2. Get a clear definition of each programming concept
3. Learn about values, variables, statements, functions, and data structures in a logical progression
4. Discover how to work with files and databases
5. Understand objects, methods, and object-oriented programming
6. Use debugging techniques to fix syntax, runtime, and semantic errors
7. Explore interface design, data structures, and GUI-based programs through case studies

Download link: Think Python: How to Think Like a Computer Scientist PDF

18. Gaussian Processes for Machine Learning

• Author: Carl Edward Rasmussen & Christopher K. I. Williams

• About This Special eBook: A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.
  Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning.

Download link: Gaussian Processes for Machine Learning PDF

19. The Data Science Handbook

• Author: William Chen, Henry Wang, Carl Shan, Max Song

• What's Special about this eBook:

The Data Science Handbook contains candid interviews with 25 of the world’s best data scientists.

This book contains insight and interviews with data scientists from established companies such as Facebook, LinkedIn, Pandora, Intuit, and The New York Times.

We also spoke with data scientists at fast-growing startups such as Uber, Airbnb, Mattermark, Quora, Square and Khan Academy

In The Data Science Handbook, You’ll learn from industry veterans such as Kevin Novak and Riley Newman, who head the data science teams at Uber and Airbnb respectively. You’ll also read about rising data scientists such as Clare Corthell, who crafted her own open source data science masters program.

Read Here: The Data Science Handbook eBook

20. R Programming for Data Science

• 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 PDF

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