Statistical Learning Applications in Python

Statistical Learning: A Comprehensive Guide

In the past two decades, there has been a surge in the amount of complex and vast data sets across various fields like biology, finance, marketing, and astrophysics. In order to make sense of this data, statistical learning has become an essential toolset. An Introduction to Statistical Learning offers a comprehensive overview of this field, providing accessible explanations of important modeling and prediction techniques essential for data analysis.

Some of the key topics covered in this book include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, and multiple testing. Real-world examples and color graphics are used throughout the book to help illustrate these techniques.

This book is suitable for statisticians and non-statisticians alike who are interested in utilizing cutting-edge statistical learning techniques to analyze their data effectively. Co-authored by four experts who previously wrote An Introduction to Statistical Learning, With Applications in R (ISLR), this new book, ISLP, aims to provide an alternative for those who prefer using Python for data analysis.

ISLP covers the same material as ISLR, but with a focus on implementing labs in Python. This decision was made in response to the rising popularity of Python in the field of data science. The labs included in this book are designed to be helpful for both beginners in Python as well as experienced users, making it a versatile resource for anyone looking to enhance their data analysis skills.

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