An Introduction to Statistical Learning: with Applications in Python (Springer Texts in Statistics) 2023rd Edition

I bought this to supplement a graduate class on the subject and very happy that I did. The content builds on itself very well, the visualizations are great and easy to read, and it goes just deep enough without it being overwhelming. It's very self contained too, I didn't feel like I had to reach for outside resources to understand a concept it was presenting. It has served as a great reference book too when needing a quick refresh on a topic.

I've studied the ESL and ISLR since they've been published; I've used ISLR as a text to teach graduate level courses and also as my guide to design a course called "Mathematics for Data Scientists" for a Master's program of a well-known University in Chicago. There's not much to say about the older editions (the precursors to this one) other that 'excellent'.About this new edition, ISLP: (1) The new chapters were wisely chosen to reflect current practice, e.g., Neural Networks. Also, the updates on older chapters is very well-done, keeping with newer terminology and better explanations; (2) The introduction of Python is, of course, a very welcome update, indeed.I have one negative comment, which has nothing to do with the content (hence my five-star review), but rather with the book binding itself. This is only relevant to the PAPERBACK edition, not the regular hardcover. It falls apart easily. After only a couple of months of use, pages are coming right off. This is very disappointing for a serious printing house like Springer. I've owned and used paperbacks of similar size heavily and for years and they are fine. With all due respect to Springer, but i feel they did a disservice to the authors and the community.All in all, highly recommended, but maybe avoid the paperback edition.

The media could not be loaded. It's been so long that I actually touched a book. All these years I've been using tablet or laptop to study. I'm really excited to go through these concepts and just immerse in the world of stats. I know basics of ML but always been curious and wanted to strengthen the fundamentals. Coming to the book, the binding is perfect and the pages are so smooth. The smell of a fresh book hits different, If you know what I'm talking about.

This is not a content review but about the bookbinding and printing quality. Like other reviewers have noted, the binding is awful. Page 1 is about to break at any moment the time I opened the book. For a Springer hardcover, I expect acid-free paper like the related book "The Elements of Statistical Learning" I bought before, but this book is printed on regular low-cost paper. It does not look like a genuine Springer hardcover book. Many Amazon paperback books are printed in local printing shops at low cost. I feel this shady practice is now extended to hardcover. I requested a replacement.

The quality is very good; printing is super clear. Anyway, this is the best machine learning text book you should have. They added deep learning in this version, and the python codes are great.

Okay, so the Python code is dated and not well explained, but it is the bible for statistics of machine learning for programmers

Excelente libroMuy buenos temas y con gran profundidad

I purchased this book for self study, with the authors’ video in YouTube, I really enjoyed the book .