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RESOURCES: Amazing course in statistical learning available freely on interwebs!

The authors of "An Introduction to Statistical Learning: with Applications in R", (Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani) have graciously made available a wealth of course materials, including videos, R labs, lecture slides, exercises, and their entire textbook. Check it out here.  Besides being a boon to those interested in understanding complex datasets with either traditional (e.g., linear regression) or newer approaches (e.g., machine learning), this kind of generous sharing of teaching resources is a model we can all aspire to... 

ISL_Cover_2.jpg
 
From the book website: "An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform."

 

  1. classification
  2. clustering coefficient
  3. course
  4. decision trees
  5. Generalized additive models
  6. linear regression
  7. logistic regression
  8. machine learning
  9. model selection
  10. MOOC
  11. principal components analysis
  12. resampling
  13. R programming language
  14. statistical learning
  15. textbook

Comments on this entry

  1. Drew LaMar

    Whoa, this looks awesome, Jeremy.  Thanks for posting.  It might be worthwhile to input some of the plethora of material available via links here into the QUBES ecosystem (e.g. the course based on this book with videos and slides).  Also, the book is now a bonafide resource on the Hub:  https://qubeshub.org/resources/505, so it can now be collected into Collections, rated, reviewed, and so forth.

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    Replying to Drew LaMar

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