This is just a short list of a few books that I have have recently discovered online.
- Model-Based Machine Learning – Chapters of this book become available as they are being written. It introduces machine learning via case studies instead of just focusing on the algorithms.
- Foundations of Data Science – This is a much more academic-focused book which could be used at the undergraduate or graduate level. It covers many of the topics one would expect: machine learning, streaming, clustering and more.
- Deep Learning Book – This book was previously available only in HTML form and not complete. Now, it is free and downloadable.
Andrew Ng [Co-Founder of Coursera, Stanford Professor, Chief Scientist at Baidu, and All-Around Machine Learning Expert] is writing a book during the summer of 2016. The book is titled, Machine Learning Yearning. It you visit the site and signup quickly you can get draft copies of the chapters as they become available.
Andrew is an excellent teacher. His MOOCs are wildly successful, and I expect his book to be excellent as well.
Professor Norm Matloff from the University of California, Davis has published From Algorithms to Z-Scores: Probabilistic and Statistical Modeling in Computer Science which is an open textbook. It approaches statistics from a computer science perspective. Dr. Matloff has been both a professor of statistics and computer science so he is well suited to write such a textbook. This would a good choice of a textbook for a statistics course targeted at primarily computer scientists. It uses the R programming language. The book starts by building the foundations of probability before entering statistics.
Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz, Associate Professor at the School of Computer
Science and Engineering at The Hebrew University, Israel, and
Shai Ben-David, Professor in the School of Computer Science at the
University of Waterloo, Canada. The book looks very thorough. Below is just a sampling of the topics covered.
- Bias-Complexity Tradeoff
- Model Selection
- Support Vector Machines
- Decision Trees
- Neural Networks
- Dimensionality Reduction
- Feature Selection and Generation
- Advanced Theory
- And LOTS LOTS more….
A great read for people without an extensive math, statistics or computer science background. And still an interesting read for those people.
The book includes tons of non-technical descriptions for data science terms.
You can download a copy of the book on SlideShare, or you can purchase a paperback copy via Lulu.
A new edition of Mining Massive Datasets is now available. It is used for a number of data mining courses at colleges across the US (and globe). Here are just a few of the topics from the book.
- Recommendation Systems
- Dimensionality Reduction
- Social Network Analysis