An Introduction to Statistical Learning
with Applications in Python
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Beschreibung
An Introduction to Statistical Learning 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, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data.
Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. 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.
An Introduction to Statistical Learning 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, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data.
Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R(ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. 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.
Presents an essential statistical learning toolkit for practitioners in science, industry, and other fields Demonstrates application of the statistical learning methods in Python Covers regression, classification, tree methods, SVM, clustering, survival analysis, deep learning
Autor*in
Gareth James
Themen in »An Introduction to Statistical Learning«
Python Python software data mining inference statistical learning supervised learning unsupervsied learning
Stimmen zu »An Introduction to Statistical Learning«
“The book is an excellent treatise on statistical learning for readers with a basic knowledge of statistics.” (Nirode C. Mohanty, zbMATH 1579.62002, 2026)
“The book adopts a hands-on, practical approach to teaching statistical learning, featuring numerous examples and case studies, accompanied by Python code for implementation. It stands as a contemporary classic, offering clear and intuitive guidance on how to implement cutting-edge statistical and machine learning methods. If you wish to intelligently use data analytics tools and techniques for analyzing big and/or complex data, this book should be front and center on your bookshelf.” (David Han, Mathematical Reviews, May 10, 2024)
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