This repo contains a list of core ML reference and learning materials
This is by no means a comprehensive list on the subject of ML/AI. However, I find that these books tackle a wide range of subjects one is likely to encounter in machine learning. Although I have included application-specific books, such as those on computer vision, I have intentionally left out others, like those in NLP. A good number of these books cover knowledge that can be applied across almost any field of machine learning. You might also note that a significant portion of these books are more focused on theory than on hands-on practice. I firmly believe that there is nothing as practical as a good theory. For more hands-on books, there are plenty available, some of which are free. I find the resources here to be particularly insightful: Datanovia Shop. Recent developments in ML can be found on arXiv as the field is evolving rapidly. I also think it's important to have a look at ML theses from different universities. It helps to see how other people approach ML. In addition to books on ML, I have added a few on mathematics. I think one gains a deeper understanding of the underlying concepts by also having a passion for pure mathematics. Some of the fields I would definitely recommend include Abstract Linear Algebra, Abstract Algebra (particularly groups), Measure Theory and Integration, Manifolds, Real, Complex, and Functional Analysis. Most of these are taught in sequence on this YouTube channel.
1. Pattern Recognition and Machine Learning - Christopher M. Bishop
2. Machine Learning: A Probabilistic Perspective - Kevin P. Murphy
3. Probabilistic Machine Learning: An Introduction - Kevin P. Murphy
4. Probabilistic Machine Learning: Advanced Topics - Kevin P. Murphy
5. Deep Learning - Ian Goodfellow, Yoshua Bengio, and Aaron Courville
6. The Elements of Statistical Learning - Trevor Hastie, Robert Tibshirani, and Jerome Friedman
7. Bayesian Reasoning and Machine Learning - David Barber
8. Probabilistic Graphical Models: Principles and Techniques - Daphne Koller and Nir Friedman
9. Information Theory, Inference, and Learning Algorithms - David J.C. MacKay
10. Pattern Classification - Richard O. Duda, Peter E. Hart, and David G. Stork
11. Understanding Machine Learning: From Theory to Algorithms - Shai Shalev-Shwartz and Shai Ben-David
12. Machine Learning Yearning - Andrew Ng
13. Artificial Intelligence: A Modern Approach - Stuart Russell and Peter Norvig
14. Machine Learning: An Algorithmic Perspective - Stephen Marsland
15. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow - Aurélien Géron
16. Deep Learning - Christopher M. Bishop
17. Applied Predictive Modeling - Max Kuhn and Kjell Johnson
18. Hamiltonian Monte Carlo Methods in Machine Learning - Tshilidzi Marwala, Rendani Mbuvha, Wilson Tsakane Mongwe
19. Probabilistic Machine Learning for Civil Engineers - James Goulet
20. Inference and Learning from Data (Vol. 1-3) - Ali H. Sayed
21. Kalman Filter from the Ground Up - Alex Becker
22. The 100-Page Machine Learning Handbook - Andriy Burkov
23. Gaussian Processes for Machine Learning - Carl Edward Rasmussen, Christopher K. I. Williams
24. Machine Learning Engineering - Andriy Burkov
25. Neural Networks for Pattern Recognition - Christopher M. Bishop
26. Understanding Deep Learning - Simon J.D. Prince
27. Reinforcement Learning, second edition: An Introduction (Adaptive Computation and Machine Learning series) - Richard S. Sutton
28. Deep Learning for Computer Vision with Python - Adrian Rosenbrock
29. Mathematics for Machine Learning - Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
30. Graph Neural Networks: Foundations, Frontiers, and Applications - Jian Pei, Liang Zhao, Lingfei Wu, Peng Cui
33. All the Mathematics You Missed: But Need to Know for Graduate School - Thomas A. Garrity, Lori Pedersen
34. Fourier Series - Georgi P. Tolstov
35. Deep Learning from the Basics to Practice - Andrew Glassner
36. Hands-On Mathematics for Deep Learning - Jay Dawani
37. All of Statistics - Larry Wasserman
38. Convex Optimization - Stephen Boyd, Lieven Vandenberghe
39. Handbook of Machine Learning (Vol 1-2) - Tshilidzi Marwala, Collins Leke
40. Computational Intelligence for Missing Data Imputation, Estimation and Management - Tshilidzi Marwala
41. Python Tricks: A Buffet of Awesome Python Features - Dan Bader
42. Professional C++ - Marc Gregoire
43. Bayesian Data Analysis - Andrew Gelman
44. High Performance Computing - John Levesque
45. Dive into Deep Learning - Aston Zhang
Please note that I am not a big fan of video tutorials, and I might have omitted some sites you love. Feel free to add those as you see fit.
I find these handy for research.