An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. This book is appropriate for anyone who wishes to use contemporary tools for …

The course for An Introduction to Statistical Learning, with Applications in Python is available here. The video lectures covering the chapter material are the same for both courses.

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The original Chapter 10 lab made use of keras, an R package for deep learning that relies on Python. Getting keras to work on your computer can be a bit of a challenge.

“With its approachable style, emphasis on practical implementation, and alignment with R labs, An Introduction to Statistical Learning has become an indispensable resource for students and …

R Materials Get the R package. Chapter 2 .R File Chapter 3 .R File Chapter 4 .R File Chapter 5 .R File Chapter 6 .R File Chapter 7 .R File Chapter 8 .R File Chapter 9 .R File Chapter 10 .R File All .R Files …

On the bottom of page 81, the sentence “Any statistical software package can be used to compute these coefficient estimates, and later in this chapter we will show how this can be done in R.” should …

I’ve been studying the English PDF version from the official website, but since many students in Latin America use this book, a Spanish version would be incredibly helpful for learning and teaching.

On page 443: immediately before Section 10.1, we plan to add the following text in the next printing: “Since the first printing of this book, the torch package has become available as an alternative to the …

NOTE: Due to a change in R’s random number generator, the results in the lab sections of ISLRv1 may not agree with the results that you obtain using a more recent version of R. This issue has been …