Christopher Bishop, a Technical Fellow at Microsoft Research, has released his textbook Pattern Recognition and Machine Learning as a free PDF download.
The book is a bit older, published in 2006, but it stills contains some great information. Some of the topics covered include:
- Probability Distributions
- Neural Networks
- Ensemble Models
- K-Means Clustering
- and many more….
Reinforcement Learning: An Introduction by Rich Sutton and Andrew Barto was recently released on October 15, 2018. The authors were kind enough to put a late draft version of the book online as a PDF. If you are hoping to learn about Reinforcement Learning, this is a great place to start.
Full text is available on a Google Drive at Reinforcement Learning. Take a look.
Good Luck and Happy Learning.
Yoshua Bengio, Ian Goodfellow and Aaron Courville are writing a deep learning book for MIT Press. The book is not yet complete, but the drafts of the chapters are all available online. The authors are also collecting comments about the chapters before the book goes to press.
The book is broken into 3 sections:
- Math and Machine Learning Fundamentals
- Modern Deep Neural Networks
- Current Research in Deep Learning
The book is very technical and probably suitable for a graduate level course. However, if you have the time and interest, resources such as this are highly valuable.
O’Reilly is releasing a free early (unedited) edition of the book, Graph Databases. The book is authored by Ian Robinson, Jim Webber, and Emil Eifrém. All three authors are members of Neo Technology, the maker of the super-popular Neo4j graph database.
This is an online, HTML version of the book, Natural Language Processing with Python. The book is a companion for NLTK which is a free, open source toolkit, written in python, for Natural Language Processing (NLP).
Cosma Shalizi of the Statistics Department at Carnegie Mellon University is working on an Advanced Data Analysis from an Elementary Point of View textbook. A copy of the textbook will remain freely available on the website. Since the textbook is still being created, comments are welcome.
The Elements of Statistical Learning textbook is available for free. It is a classic, widely-used textbooks for statistics and machine learning. Here is a far from complete list of some of the topics:
- Supervised Learning
- Linear/Logistic Regression
- Model Selection
- Neural Networks
- Support Vector Machines
- Random Forests
- Unsupervised Learning
As you can see, the book is quite extensive.
Note: This book has been available for a quite a while, but I realized I have not added a link to it on my blog.