54 Must-Read Machine Learning E-Books

Machine learning is a rapidly evolving field, and staying updated with the latest knowledge is crucial. Here is a list of 54 must-read e-books, gathered from various sources, that can help you understand and master machine learning. Some of These books are available for free online, providing a valuable resource for anyone interested in this field.

1. Deep Learning

Authors: Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Description: A comprehensive resource for students and practitioners entering the field of deep learning.
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2. Mathematics for Machine Learning

Authors: Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
Description: Introduces the mathematical concepts needed to understand machine learning, bridging the gap between mathematical and machine learning texts.
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3. An Introduction to Statistical Learning

Authors: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor
Description: Provides a broad and less technical treatment of key topics in statistical learning.
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4. The Elements of Statistical Learning

Authors: Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie
Description: Covers important ideas in various fields using a common conceptual framework, with an emphasis on concepts rather than mathematics.
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5. Probabilistic Machine Learning: An Introduction

Authors: Kevin Patrick Murphy
Description: Offers a detailed introduction to machine learning through probabilistic modeling and Bayesian decision theory.
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6. Probabilistic Machine Learning: Advanced Topics

Authors: Kevin Patrick Murphy
Description: Covers advanced topics in machine learning, including deep generative modeling, graphical models, and Bayesian inference.
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7. Understanding Machine Learning: From Theory to Algorithms

Authors: Shai Shalev-Shwartz and Shai Ben-David
Description: Introduces machine learning and algorithmic paradigms in a principled way, providing a theoretical account of fundamental ideas.
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8. Automated Machine Learning: Methods, Systems, Challenges

Authors: Frank Hutter, Lars Kotthoff, Joaquin Vanschoren
Description: Presents a comprehensive overview of general methods in Automated Machine Learning (AutoML).
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9. Applied Causal Inference

Authors: Uday Kamath, Kenneth Graham, Mitchell Naylor
Description: Designed to help users along the spectrum of experience with causal inference, from beginners to experienced users.
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10. Reinforcement Learning: An Introduction

Authors: Richard S. Sutton and Andrew G. Barto
Description: Provides a clear and simple account of the field's key ideas and algorithms, with expanded topics.
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11. The Hundred-Page Machine Learning Book

Authors: Andriy Burkov
Description: Condenses machine learning into 100 pages, providing a solid introduction to the field.
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12. Machine Learning Engineering

Authors: Andriy Burkov
Description: Filled with best practices and design patterns for building reliable machine learning solutions that scale.
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13. Natural Language Processing with Python

Authors: Steven Bird, Ewan Klein, and Edward Loper
Description: Offers an accessible introduction to natural language processing, supporting a variety of language technologies.
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14. Dive into Deep Learning

Authors: Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola
Description: A comprehensive resource making deep learning approachable while providing technical depth.
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15. Machine Learning Yearning

Authors: Andrew NG
Description: Learn how to align on ML strategies in a team setting and set up development and test sets.
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16. Machine Learning for Humans

Authors: Vishal Maini, Samer Sabri
Description: Covers basic concepts in probability, statistics, programming, linear algebra, and calculus, accessible to anyone.
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17. Pattern Recognition and Machine Learning

Authors: Christopher M. Bishop
Description: The first textbook on pattern recognition to present the Bayesian viewpoint, using graphical models.
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18. Deep Learning on Graphs

Authors: Yao Ma and Jiliang Tang
Description: Comprehensive content on developing deep learning techniques for graph-structured data, focusing on Graph Neural Networks (GNNs).
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19. Approaching (Almost) Any Machine Learning Problem

Authors: Abhishek Thakur
Description: Practical guide for solving machine learning problems, focusing on how and what to use.
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20. Speech and Language Processing

Authors: Daniel Jurafsky & James Martin
Description: Suitable for undergraduate or advanced undergraduate courses in various natural language processing topics.
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21. Computer Vision: Models, Learning, and Inference

Authors: Simon J.D. Prince
Description: Focuses on learning and inference in probabilistic models, showing how to use training data to learn relationships between observed image data and aspects of the world.
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22. Information Theory, Inference and Learning Algorithms

Authors: David J. C. MacKay
Description: Introduces theory in tandem with applications, teaching information theory alongside practical communication systems.
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23. Machine Learning: A Probabilistic Perspective

Authors: Kevin P. Murphy
Description: A detailed and up-to-date introduction to machine learning from a probabilistic perspective.
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24. Bayesian Reasoning and Machine Learning

Authors: David Barber
Description: Provides an introduction to the main ideas and techniques of Bayesian machine learning.
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25. Machine Learning Yearning

Authors: Andrew Ng
Description: Teaches how to structure machine learning projects.
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26. Neural Networks and Deep Learning

Authors: Michael Nielsen
Description: An online book aiming to teach deep learning using clear, simple explanations.
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27. Deep Learning for Computer Vision

Authors: Rajalingappaa Shanmugamani
Description: Focuses on deep learning techniques for computer vision.
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28. Fundamentals of Deep Learning

Authors: Nikhil Buduma and Nicholas Locascio
Description: Introduces the fundamental concepts and applications of deep learning.
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29. Applied Machine Learning

Authors: David Forsyth
Description: Covers practical machine learning topics with a focus on applications.
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30. Data Science from Scratch

Authors: Joel Grus
Description: Introduces the concepts of data science, including machine learning, from scratch.
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31. Learning from Data

Authors: Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin
Description: Provides a comprehensive introduction to the topic of learning from data.
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32. Deep Learning for Beginners

Authors: Taylor Smith
Description: Simplifies the concepts of deep learning for beginners.
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33. Understanding Machine Learning

Authors: Shai Shalev-Shwartz and Shai Ben-David
Description: Aims to introduce machine learning and the algorithmic paradigms it offers in a principled way.
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34. Deep Learning with Python

Authors: François Chollet
Description: Teaches deep learning using Python and the Keras library.
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35. Machine Learning for Humans

Authors: Vishal Maini, Samer Sabri
Description: Covers fundamental machine learning concepts in an easy-to-understand manner.
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36. Machine Learning with Python Cookbook

Authors: Chris Albon
Description: Offers practical solutions for Python developers working on machine learning projects.
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37. Interpretable Machine Learning

Authors: Christoph Molnar
Description: Explains how to make machine learning models interpretable.
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38. Machine Learning: The Art and Science of Algorithms that Make Sense of Data

Authors: Peter Flach
Description: Provides an introduction to machine learning through algorithms and their practical applications.
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39. Probabilistic Graphical Models: Principles and Techniques

Authors: Daphne Koller and Nir Friedman
Description: Introduces the principles and techniques of probabilistic graphical models.
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40. Machine Learning with R

Authors: Brett Lantz
Description: Provides a hands-on guide to applying machine learning with R.
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41. Python Machine Learning

Authors: Sebastian Raschka and Vahid Mirjalili
Description: Covers machine learning using Python, focusing on libraries like scikit-learn and TensorFlow.
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42. Deep Learning with R

Authors: François Chollet and J. J. Allaire
Description: Combines the capabilities of R with the Keras deep learning library.
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43. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Authors: Aurélien Géron
Description: Provides practical, hands-on examples to understand machine learning concepts using popular Python libraries.
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44. Neural Networks and Deep Learning

Authors: Charu C. Aggarwal
Description: Introduces neural networks and deep learning, focusing on applications.
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45. Deep Learning: A Practitioner's Approach

Authors: Adam Gibson and Josh Patterson
Description: Provides a practitioner's approach to implementing deep learning models.
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46. Deep Reinforcement Learning Hands-On

Authors: Maxim Lapan
Description: Offers practical solutions and explanations for deep reinforcement learning.
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47. Artificial Intelligence: A Modern Approach

Authors: Stuart Russell and Peter Norvig
Description: The standard text in AI, covering broad aspects of artificial intelligence, including machine learning.
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48. Data Mining: Practical Machine Learning Tools and Techniques

Authors: Ian H. Witten, Eibe Frank, Mark A. Hall
Description: Introduces data mining and machine learning tools and techniques.
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49. Python Machine Learning By Example

Authors: Yuxi (Hayden) Liu
Description: Provides practical examples and solutions for machine learning using Python.
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50. Machine Learning and Data Science Blueprints for Finance

Authors: Hariom Tatsat, Sahil Puri, Brad Lookabaugh
Description: Focuses on applying machine learning and data science techniques in finance.
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51. The Practitioner's Guide to Graph Data

Authors: Denise Gosnell and Matthias Broecheler
Description: Focuses on graph data structures and their applications in machine learning.
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52. Feature Engineering for Machine Learning

Authors: Alice Zheng and Amanda Casari
Description: Introduces feature engineering techniques for improving machine learning models.
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53. Deep Learning for Natural Language Processing

Authors: Palash Goyal, Sumit Pandey, Karan Jain
Description: Explores deep learning techniques for natural language processing.
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54. Machine Learning: Hands-On for Developers and Technical Professionals

Authors: Jason Bell
Description: A practical guide for implementing machine learning models.
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