Trevor Hastie and Robert Tibshirani are teaching an online class on Statistical Learning starting this week.

The first week is introduction and overview, so it’s not too late to join up.

They’ve also published a new book, An Introduction to Statistical Learning, as a more accessible companion to their widely revered The Elements of Statistical Learning. Like it’s older sibling, the new book is availabe for free download as a PDF.

The class overlaps a bit with Andrew Ng’s Machine Learning class, but I’m looking forward to a different perspective, new material on penalized regression, resampling methods and non-linear fitting and random forest, and more practice.

The Statistical Learning class is taught with examples in R, which is great.

Amir Sadoughi is starting a community driven solution guide to the exercises.

If you prefer Python, some folks at Boston startup DataRobot is planning to follow the class with a series of blog posts that show how “statistical learning techniques presented in the course can be applied using tools from the Python ecosystem: “numpy”, “scipy”, “pandas”, “matplotlib”, “scikit-learn”, and “statsmodels”“. Awesome!

Those interested may also like Yaser Abu-Mostafa’s MOOC Learning from Data which ran “live” last year but is now available in “take at your own pace” mode. I haven’t taken it, but have heard glowing recommendations. Students for that course produced a truely impressive solutions guide with code in R, Python, Octave, Haskell and several other languages.

For those who want and have a budget for the in-person experience Hastie and Tibshirani are teaching a 2 day seminar in Palo Alto on March 20-21.