35 Free Online Books on Machine Learning

• Pattern Recognition & Machine Learning
• Probability & Statistics
• Neural Networks & Deep Learning

List of 35 Free eBooks on Machine Learning & Related Fields

Following is a list of 35 FREE online ebooks (pdf format) which could be used for learning ML at your own pace.

• Pattern Recognition & Machine Learning
  1. Foundations of Machine Learning
  2. The Elements of Statistical Learning – Trever Hastie, Robert Tibshirani, Jerome Friedman
  3. Machine Learning: A Probabilistic Approach: Authored by Kevin P. Murphy, the summary details of this book could be found on following page.
  4. 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.
  5. Information Theory, Inference, and Learning Algorithms (David Mackay)
  6. Pattern Recognition: Authored by Sergios Theodoridis, Konstantinos Koutroumbas
  7. A Probabilistic Theory of Pattern Recognition. Devroye, Gyorfi, Lugosi.
  8. Introduction to Machine Learning. Smola and Vishwanathan
  9. Machine Learning and Bayesian Reasoning. David Barber
  10. Gaussian Processes for Machine Learning. Rasmussen and Williams
  11. Introduction to Information Retrieval. Manning, Rhagavan, Shutze
  12. Forecasting: principles and practice. Hyndman, Athanasopoulos. (Online Book)
  13. Introduction to Machine Learning; Shashua
  14. Reinforcement Learning; Weber et al.
  15. Machine Learning; Mellouk & Chebira
  16. Bayesian Reasoning and Machine Learning
  17. Probabilistic Programming and Bayesian Methods for Hackers
  18. A Course in Machine Learning
  19. Data Mining: Practical Machine Learning Tools and Techniques
  20. Machine Learning Evaluation: A Classification Perspective
  21. Introduction to Machine Learning in Python with scikit-learn
  22. The LION Way: Machine Learning plus Intelligent Optimization – Roberto Battiti, Mauro Brunato
  23. A First Encounter with Machine Learning – Max Welling
  24. Practical Artificial Intelligence Programming in Java – Mark Watson
  25. Machine Learning – The Art & Science of Algorithms that Make Sense of Data – Peter Flach
• 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
• Neural Networks & Deep Learning
  1. 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.
  2. 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.
  3. Unsupervised Feature Learning and Deep Learning
  4. Machine Learning, Neural and Statistical Classification; Michie & Spiegelhalter
  5. Machine Learning, Neural and Statistical Classification – D. Michie, D. J. Spiegelhalter