Introduction to statistical learning, 2nd edition

Looks like a few new chapters. e.g Deep Learning<p>I own the first edition. Made it through the entire book during the pandemic with a study partner.<p>There are other resources:<p><a href="https:&#x2F;&#x2F;github.com&#x2F;melling&#x2F;ISLR" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;melling&#x2F;ISLR</a>

&quot;In this second edition of ISL, we have greatly expanded the set of topics covered. In particular, the second edition includes new chapters on deep learning (Chapter 10), survival analysis (Chapter 11), and multiple testing (Chapter 13). We have also substantially expanded some chapters that were part of the first edition: among other updates, we now include treatments of naive Bayes and generalized linear models in Chapter 4, Bayesian additive regression trees in Chapter 8, and matrix completion in Chapter 12. Furthermore, we have updated the R code throughout the labs to ensure that the results that they produce agree with recent R releases.&quot;

Thanks for sharing. Not one to look a gift horse in the mouth, but would have really preferred if they had used Python&#x2F;Pandas instead of R.<p>But still, can&#x27;t complain. I bought the very first edition back in my Uni days - look forward to revisiting the new edition.

This is great. I read the first edition cover to cover years ago as I first got interested in data science. 6 years and 3 data scientist jobs later and I still find myself going back from time to time.

Surprised to see it still uses R rather than Python

Amazing book. I always recommend this book due its clear and concise explanations.<p>I look forward to reading the second edition.

This is a fantastic book. I took Hastie and Tibsurani’s class years ago, and I still refer to this text.

I always recommend this book when people ask me about learning statistics and ML.