Machine Learning is a type of artificial intelligence (AI) that provides computer programs with the ability to learn, grow and change when exposed to new data, without being explicitly programmed. The process of Machine Learning is similar to that of data mining. Both systems search through data to look for patterns. However, instead of extracting data for human comprehension — as is the case in data mining applications — machine learning uses that data to improve the program’s own understanding. Machine Learning programs detect patterns in data and adjust program actions accordingly. Here, we present 20 free eBooks on Machine Learning, which will guide you to understand more about Machine Learning.
1. A Course in Machine Learning by Hal Daumé III
2. A First Encounter with Machine Learning by Max Welling
3. An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
4. Bayesian Reasoning and Machine Learning by David Barber
5. Gaussian Processes for Machine Learning by Carl Edward Rasmussen and Christopher K. I. Williams
6. How Are We To Know? by Nils J. Nilsson
7. Inductive Logic Programming Techniques and Applications by Nada Lavrac and Saso Dzeroski
8. Inductive Logic Programming: Techniques and Applications by Nada Lavrac, Saso Dzeroski
9. Information Theory, Inference, and Learning Algorithms by David J.C. MacKay
10. Introduction to Machine Learning by Amnon Shashua
11. Introduction To Machine Learning by Nils J Nilsson
12. Machine Learning by Abdelhamid Mellouk and Abdennacer Chebira
13. Machine Learning plus Intelligent Optimization by Roberto Battiti, Mauro Brunato
14. Machine Learning, Neural and Statistical Classification by D. Michie, D.J. Spiegelhalter, C.C. Taylor
15. Practical Artificial Intelligence Programming in Java by Mark Watson
16. Reinforcement Learning by C. Weber, M. Elshaw, N. M. Mayer
17. Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
18. The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, Jerome Fried
