Machine Learning Books (Mar 2020)

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Putting the books listed below into three categories based on complexity

I: Mathematical Theory

• 1. Deep Learning
  Book by Aaron Courville, Ian Goodfellow, and Yoshua Bengio
• 5. Pattern Recognition and Machine Learning
  Book by Christopher Bishop

II: Mix of Theory and Applied Study

• 2. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
  Concepts, Tools, and Techniques to Build Intelligent Systems
  Book by Aurelien Geron
• 54. Applied Machine Learning and AI for Engineers
  Solve Business Problems That Can't Be Solved Algorithmically (Release 1)
  Jeff Prosise
  O’Reilly Media, Inc. (2022)

III: Applied Study

• 6. The Hundred-Page Machine Learning Book
  Book by Andriy Burkov
• 9. Machine Learning for Absolute Beginners: A Plain English Introduction
  Book by O. Theobald
• 52. Machine Learning in Python (Essential Techniques For Predictive Analysis)
  By: Michael Bowles
• 53. Fifty Algorithms Every Programmer Should Know (2e)
  By: Imran Ahmad (PhD)

All

1. Deep Learning
Book by Aaron Courville, Ian Goodfellow, and Yoshua Bengio

2. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Concepts, Tools, and Techniques to Build Intelligent Systems
Book by Aurelien Geron

3. The Elements of Statistical Learning
Book by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie

4. An Introduction to Statistical Learning: With Applications in R
Book

5. Pattern Recognition and Machine Learning
Book by Christopher Bishop

6. The Hundred-Page Machine Learning Book
Book by Andriy Burkov

7. Deep Learning with Python
Book by François Chollet

8. Understanding Machine Learning: From Theory to Algorithms
Textbook by Shai Ben-David and Shai Shalev-Shwartz

9. Machine Learning for Absolute Beginners: A Plain English Introduction
Book by O. Theobald

10. Python Machine Learning
Book by Sebastian Raschka

11. Artificial Intelligence: A Modern Approach
Textbook by Peter Norvig and Stuart J. Russell

12. Introduction to Machine Learning
Textbook by Ethem Alpaydın

13. Machine Learning: A Probabilistic Perspective
Textbook by Kevin P. Murphy

14. Machine Learning for Hackers
Book by Drew Conway and John Myles White

15. Programming Collective Intelligence
Book: O'Reilly

16. Machine Learning For Dummies
Book by John Mueller and Luca Massaron

17. Bayesian Reasoning and Machine Learning
Book by David Barber

18. Reinforcement Learning: An Introduction
Book by Andrew Barto and Richard S. Sutton

19. Learning from Data: A Short Course
Book by Hsuan-Tien Lin, Malik Magdon-Ismail, and Yaser Abu-Mostafa

20. Machine Learning in Action
Book by Peter Harrington

21. Machine Learning: The Art and Science of Algorithms that Make Sense of Data
Book by Peter Flach

22. Introduction to Machine Learning with Python: A Guide for Data Scientists
Book by Andreas C. Müller and Sarah Guido

23. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies
Textbook by Aoife D'Arcy, Brian Mac Namee, and John D. Kelleher

24. Mining of Massive Datasets
Book by Anand Rajaraman and Jeffrey Ullman

25. Foundations of Machine Learning
Textbook by Afshin Rostamizadeh, Ameet Talwalkar, and Mehryar Mohri

26. Superintelligence: Paths, Dangers, Strategies
Book by Nick Bostrom

27. Make Your Own Neural Network: A Gentle Journey Through the Mathematics ...
Book by Tariq Rashid

28. Python Machine Learning: Machine Learning and Deep Learning with Python, Scikit-learn, and TensorFlow 2, 3rd Edition
Book by Sebastian Raschka and Vahid Mirjalili

29. Machine Learning: An Algorithmic Perspective
Book by Stephen Marsland

30. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
Book by Pedro Domingos

31. Grokking Deep Learning
Book by Andrew W. Trask

32. Advances in Financial Machine Learning
Book by Marcos Lopez de Prado

33. Machine Learning: A Guide to Current Research
Book by Tom M. Mitchell

34. Pattern Classification
Book by David G. Stork, Peter E. Hart, and Richard O. Duda

35. Building Machine Learning Systems with Python - Second Edition
Book by Luis Pedro Coelho and Willi Richert

36. Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference
Book by Cameron Davidson-Pilon

37. Information Theory, Inference and Learning Algorithms
Textbook by David J. C. MacKay

38. Probabilistic Graphical Models: Principles and Techniques
Book by Daphne Koller and Nir Friedman

39. Interpretable Machine Learning
Book by Christoph Molnar

40. The Book of Why: The New Science of Cause and Effect
Book by Dana Mackenzie and Judea Pearl

41. Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms
Book by Nicholas Locascio and Nikhil Buduma

42. Deep Reinforcement Learning Hands-On: Apply Modern RL Methods, with Deep Q-networks, Value Iteration, Policy Gradients, TRPO, AlphaGo Zero and More

43. Think Stats
Book by Allen B. Downey

44. Gaussian Processes for Machine Learning
Book by Carl Edward Rasmussen and Christopher K. I. Williams

45. Data Mining: Practical Machine Learning Tools and Techniques
Book

46. Machine Learning with R
Book by Brett Lantz

47. Python Data Science Handbook: Essential Tools for Working with Data
Book by Jake VanderPlas

48. Real world machine learning: video edition
Book by Henrik Brink, Joseph Richards, and Mark Fetherolf

49. Machine Learning Algorithms: Popular Algorithms for Data Science and Machine Learning
Book by Giuseppe Bonaccorso

50. Machine Learning: A Bayesian and Optimization Perspective
Book by Sergios Theodoridis

51. Mathematics for Machine Learning
Textbook by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth

52. Machine Learning in Python (Essential Techniques For Predictive Analysis)
By: Michael Bowles

53. Fifty Algorithms Every Programmer Should Know (2e)
By: Imran Ahmad (PhD)

54. Applied Machine Learning and AI for Engineers 
Solve Business Problems That Can't Be Solved Algorithmically (Release 1)
Jeff Prosise
O’Reilly Media, Inc. (2022)

Tags: List of Books,Machine Learning,