• Title: An Introduction to Statistical Learning: with Applications in Python
• Author(s) Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
• Publisher: Springer; 1st ed. 2023 edition (September 8, 2023); eBook (July 5, 2023)
• Hardcover: 619 pages
• eBook: PDF (613 pages)
• Language: English
• ISBN-10: 3031391896
• ISBN-13: 978-3031391897
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Book Description

This book 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.

in recent years Python has become a popular language for data science, hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

• Gareth James is the E. Morgan Stanley Chair in Business Administration and a professor of data sciences and operations at the Marshall School of Business at the University of Southern California.
• Daniela Witten is a professor of statistics and biostatistics at the University of Washington.
• Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University, and are co-authors of the successful textbook Elements of Statistical Learning.

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• Machine Learning
• The Python Programming Language
• Statistics, Mathematical Statistics, and SAS Programming
• Data Analysis and Data Mining
• Artificial Intelligence

• An Introduction to Statistical Learning: with Applications in Python (Gareth James, et al.)
• Lecture Slides, Videos, Interviews, etc.
• Book Homepage (R and Python Editions, Errata, Resources, etc.)

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