Machine Learning free Books Online
Here is a list of online Books related to the subject of Machine Learning in various formats available for free :
- Elements of Statistical Learning -Trevor Hastie, Robert Tibshirani, and Jerome Friedman
- A Probabilistic Theory of Pattern Recognition – Devroye, Gyorfi, Lugosi.
- Introduction to Information Retrieval – Manning, Rhagavan, Shutze
- Forecasting: principles and practice – Rob J Hyndman George Athanasopoulos (Online Book)
- Neural Networks and Deep Learning – Nielsen
- Supervised Sequence Labelling with Recurrent Neural Networks – Graves
- Reinforcement Learning: An Introduction; 2nd Edition – Richard S. Sutton and Andrew G. Barto
- Pattern Recognition and Machine Learning – Christopher Bishop
- A Brief Introduction to Neural Networks – David Kriesel
- A Course in Machine Learning – Hal Daumé III
- Machine Learning: a Probabilistic Perspective -Kevin Patrick Murphy
- Machine Learning and Bayesian Reasoning – David Barber
- Gaussian Processes for Machine Learning -Carl Edward Rasmussen and Christopher K. I. Williams
- Introduction to Machine Learning -Alex Smola and S.V.N. Vishwanathan
- A First Encounter with Machine Learning – Max Welling
- Deep Learning – Ian Goodfellow, Yoshua Bengio and Aaron Courville
- Introduction to Machine Learning – Amnon Shashua
- Learning Deep Architectures for AI – Yoshua Bengio
- Machine Learning – Abdelhamid Mellouk and Abdennacer Chebira
- Machine Learning, Neural and Statistical Classification [link 2]– D. Michie, D.J. Spiegelhalter, C.C. Taylor
- Probabilistic Models in the Study of Language – Roger Levy
- Information Theory, Inference, and Learning Algorithms – David J C MacKay
- Data Intensive Text Processing w/ MapReduce – Jimmy Lin and Chris Dyer.
- Mining Massive Datasets – Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman
- Reinforcement Learning – Edited by Cornelius Weber, Mark Elshaw and Norbert Michael Mayer
- Introduction to Applied Bayesian Statistics and Estimation for Social Scientists – Scott M. Lynch
- R Programming for Data Science – Roger D. Peng
