85+ Free Online Books, Courses – Machine Learning & Data Science

• Data science
• Pattern Recognition & Machine Learning
• Probability & Statistics
• Neural Networks & Deep Learning
• Optimization
• Data mining
• Mathematics

Here is my post on machine learning concepts & examples.

List of online free eBooks & Courses on Machine Learning, Data Science, Deep Learning

Following is a list of 85+ FREE online ebooks (pdf format) and video courses that could be used for learning ML at your own pace. Note that these books are placed in random order and thus there is no meaning to the current order. Hence, I would recommend you go through different books and select the one which suits you most.

• Data Science
  1. The Art of Data Science by Roger D Peng and Elizabeth Matsui. The book is available for free of cost in case you choose to grab it for free. However, you would need to register with the website. The book describes the process of data analysis.
  2. Data Science handbook: A compilation of over 25 data scientists views/insights on data science
  3. Python data science handbook
  4. The Field Guide to Data Science by Booz Allen Hamilton
  5. Data Science for Business by Tom Fawcett and Foster Provost
  6. Building data science teams by DJ Patil
  7. Free online data science courses from Harvard university (R basics, visualization, probability, TinyML, etc
  8. Free data science & machine learning courses by Stanford university (Statistics, R programming fundamentals, machine learning specialization)
• Pattern Recognition & Machine Learning
  1. AWS Machine Learning Foundations Course (Free course offered at Udacity)
  2. Planning algorithms
  3. The hundred-page machine learning book by Andriy Burkov
  4. An introduction to variable and feature selection – Isabelle G. and Andre E
  5. Building machine learning systems with Python
  6. Scikit tutorial – statistical learning for scientific data processing
  7. Foundations of Machine Learning
  8. Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David: The book introduces machine learning with the help of mathematical derivations.
  9. The Elements of Statistical Learning – Trever Hastie, Robert Tibshirani, Jerome Friedman
  10. Machine Learning: A Probabilistic Approach: Authored by Kevin P. Murphy, the summary details of this book could be found on the following page.
  11. Pattern Recognition & Machine Learning – Christopher M. Bishop: This book is a great book but if you are not the one who loves Maths, it may go out and scare you enough. 🙂 So, get your mathematics fundamentals good enough and get started with it.
  12. Information Theory, Inference, and Learning Algorithms (David Mackay)
  13. Pattern Recognition: Authored by Sergios Theodoridis, Konstantinos Koutroumbas
  14. A Probabilistic Theory of Pattern Recognition. Devroye, Gyorfi, Lugosi.
  15. Introduction to Machine Learning. Smola and Vishwanathan
  16. Machine Learning and Bayesian Reasoning. David Barber
  17. Gaussian Processes for Machine Learning. Rasmussen and Williams
  18. Introduction to Information Retrieval. Manning, Rhagavan, Shutze
  19. Forecasting: principles and practice. Hyndman, Athanasopoulos. (Online Book)
  20. Introduction to Machine Learning; Shashua
  21. Reinforcement Learning; Weber et al.
  22. Machine Learning; Mellouk & Chebira
  23. Bayesian Reasoning and Machine Learning
  24. Probabilistic Programming and Bayesian Methods for Hackers
  25. A Course in Machine Learning
  26. Machine Learning Evaluation: A Classification Perspective
  27. Introduction to Machine Learning in Python with scikit-learn
  28. The LION Way: Machine Learning plus Intelligent Optimization – Roberto Battiti, Mauro Brunato
  29. A First Encounter with Machine Learning – Max Welling
  30. Practical Artificial Intelligence Programming in Java – Mark Watson
  31. Machine Learning – The Art & Science of Algorithms that Make Sense of Data – Peter Flach
  32. Think Bayes
  33. Natural language processing with Python
  34. Artificial Intelligence: Foundations of Computational Agents, 2nd Edition by David Poole, Alan Mackworth
  35. Machine learning yearning by Andrew Ng
  36. Seven steps to success – machine learning in practice
  37. Rules for machine learning: Best practices for ML engineering
  38. A brief introduction to machine learning for Engineers
  39. AutoML Book – Frank Hutter, Lars Kotthoff, Joaquin Vanschoren
  40. Model-based machine learning – John Winn
  41. Reinforcement learning – An introduction – Richard Sutton and Andrew Burto
  42. Algorithmic aspects of machine learning – Ankur Moitra
  43. Cluster analysis by Prof. Thomas B. Fomby
  44. Effective learning machines by Mariette Awad, Rahul Khanna
  45. Automated machine learning by Frank Hutter, Lars Kotthoff, Joaquin Vanschoren
  46. Introduction to machine learning course @ Udacity
• Probability & Statistics
  1. All of Statistics: Authored by L. Wasserman, the details of this book could be further found on this page.
  2. Introduction to statistical thought. Lavine
  3. Basic Probability Theory. Robert Ash
  4. Introduction to probability. Grinstead and Snell
  5. Stanford Statistics Learning Class – Lecture Notes
  6. Statistical Learning with Sparsity: The Lasso and Generalizations by Trevor Hastie, Robert Tibshirani, Martin Wainwright
  7. An Introduction to Statistical Learning by Gareth M. James, Daniela Witten, Trevor Hastie, Robert Tibshirani
  8. Think Stats by Allen B Downey: Provides an introduction to Probability and Statistics for Python programmers.
  9. Statistical learning and sequential prediction by Alexander Rakhlin and Karthik Sridharan
  10. Research on teaching and learning probability by Carmen Batanero, Egan J. Chernoff, Joachim Engel, Hollylynne S. Lee, Ernesto Sánchez
• Neural Networks & Deep Learning
  1. Deep learning book by Ian Goodfellow and Yoshua Bengio
  2. Draft Textbook on Deep Learning: This is a draft textbook from Yoshua Bengio, Ian Goodfellow and Aaron Courville is the most comprehensive treatment of deep learning.
  3. Neural Networks and Deep Learning: Free draft e-book entitled “Neural Networks and Deep Learning” authored by Michael Nielsen whose work could be found on his personal website, MichaelNielson.org.
  4. Unsupervised Feature Learning and Deep Learning
  5. Machine Learning, Neural and Statistical Classification; Michie & Spiegelhalter
  6. Machine Learning, Neural and Statistical Classification – D. Michie, D. J. Spiegelhalter
  7. Deep Learning with Python: Book by Francois Chollet. This is a Github page consisting of source code implemented in Google Collab. Here is the link for the PDF.
  8. Deep Learning (Adaptive Computation and Machine Learning series) by IAN Goodfellow
  9. Introduction to TensorFlow for AI, machine learning and deep learning (free Coursera course)
  10. Introduction to Tensorflow for deep learning (Free course on Udacity)
  11. Deeplearning.AI Tensorflow developer professional certificate course (free Coursera course)
  12. A brief introduction to neural networks by D Kriesel
  13. Deep dive into Deep learning – Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola
  14. Google AI for Javascript developers with Tensorflow.js
  15. Introduction to deep learning – MIT 6.S191 – Free course

• Optimization
  1. Convex Optimization 1st Edition by Stephen Boy
  2. Optimization for machine learning by Gabriel Peyre
• Data mining
  1. Data Mining and Analysis by Mohammed J. Zaki and Wagner Meira, Jr
  2. Mining of massive datasets by Jure Leskovec, Anand Rajaraman, Jeff Ullman
  3. Data Mining: Practical Machine Learning Tools and Techniques
• Mathematics
  1. Single variable calculus (MIT) – Prof. David Jv – Course
  2. Linear algebra (MIT) – Prof. Gilbert S – Course