An Introduction to Statistical Learning: With Applications in Python (Springer Texts in Statistics)

The paperback version is costlier for a reason, the paper quality is stellar and colour print is useful to read and understand points on the chart which the authors would like to highlight in the relevant chapter. Excited to learn!

Book is awesome but still waiting for the cheaper paper back option Please bring that one asap 3k is too costly

Book bindings is very poor

Book has more than half of it in black and white print. And few in color print. The replacement request is not fulfilled and it's past one week of the due date. The replacement is still an open order.

The description states that "Color graphics and real-world examples are used to illustrate the methods presented." This book is better read in colour, as there are a lot of graphs, and b/w diagrams are confusing. Better to use online free pdf from author. For 2000 was expecting colour book.

The contents of the book are in black and white

Book packaging was perfect and book condition was very good, but problem was it was not in color as mentioned. The drawing description says "the blue line...." or "the green line..." But images are b&w. Not able to understand. Got disappointed and returned.

The review is not about the content of the book but the quality. Paper quality is used extremely bad.

Excelent book. It offers a gentle introduction on the subject, without using overwhelming math or theory. A must read for anyone interested on this area.

Excelente libro, muy práctico para los interesados en ciencia de datos o analista de datos. Tiene un enfoque práctico enfocado a ejemplos usando Python. NO es teorico, pero si explican los conceptos. Es una compra obligada si te interesa la estadística y programar con Python

Excellent!

Just the perfect introduction to Machine Learning ...

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.