• 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
• Share This:  

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.

• Amazon
• Amazon (An Introduction to Statistical Learning: with Applications in R)

• 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.)

• Statistics and Machine Learning in Python (Edouard Duchesnay)

  Illustrates the fundamental concepts that link statistics and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses.

• Statistics and Machine Learning in Python (Edouard Duchesnay)

  Illustrates the fundamental concepts that link statistics and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses.

• The Elements of Statistical Learning: Data Mining, Inference, etc.

  This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics.

• Introduction to Statistical Learning: with Applications in R

  It 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.

• Statistical Learning and Sequential Prediction

  This book focuses on theoretical aspects of Statistical Learning and Sequential Prediction, a unified approach to analyzing learning in both scenarios, brings together ideas from probability and statistics, game theory, algorithms, and optimization.

• Statistical Foundations of Machine Learning: The Handbook

  This book aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. All the examples are implemented in the statistical programming language R.

• Probabilistic Machine Learning: An Introduction (Kevin Murphy)

  This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. It is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.

• Bayesian Methods for Hackers: Probabilistic Programming

  This book illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, Matplotlib, through practical examples and computation - no advanced mathematics required.

• Pattern Recognition and Machine Learning (Christopher Bishop)

  This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible.

• Foundations of Machine Learning (Mehryar Mohri, et al)

  This book is a general introduction to machine learning. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms.

• The Hundred-Page Machine Learning Book (Andriy Burkov)

  Everything you really need to know in Machine Learning in a hundred pages! This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori.