Free Statistical Learning Course from Stanford

Here is a great statistical course using R offered from Stanford for free starting now!

A brief description of the course from the source site.

This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).

This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data analysis. Computing is done in R. There are lectures devoted to R, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chapter.

The lectures cover all the material in An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani (Springer, 2013). The pdf for this book is available for free on the book website.


Welcome to my slice of the web!

This blog is dedicated to my experience with Microsoft Azure Machine Learning and predictive analytics in an attempt to help others leverage the product for their needs.

I first heard about Azure ML during a hackathon/POC we had in Redmond, WA, with Microsoft Azure evangelists, Rob Bagby, Jesus Aguilar and the rest of their team.

This was a great event that covered a lot of Microsoft technologies from the inside of Building 20, Microsoft’s campus.  Having access to Microsoft’s  engineers during the event provided invaluable experience.