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Springer Texts in Statistics Ser.: Introduction to Statistical Learning : With Applications in Python by Trevor Hastie, Gareth James, Robert Tibshirani, Jonathan Taylor and Daniela Witten (2024, Trade Paperback)

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These labs will be useful both for Python novices, as well as experienced users.

About this product

Product Identifiers

PublisherSpringer International Publishing A&G

ISBN-103031391896

ISBN-139783031391897

eBay Product ID (ePID)7061474684

Product Key Features

Number of PagesXv, 607 Pages

LanguageEnglish

Publication NameIntroduction to Statistical Learning : with Applications in Python

Publication Year2024

SubjectMathematical & Statistical Software, Probability & Statistics / General, General

TypeTextbook

Subject AreaMathematics, Computers

AuthorTrevor Hastie, Gareth James, Robert Tibshirani, Jonathan Taylor, Daniela Witten

SeriesSpringer Texts in Statistics Ser.

FormatTrade Paperback

Dimensions

Item Length10 in

Item Width7 in

Additional Product Features

TitleLeadingAn

Reviews"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)

Dewey Edition23

Number of Volumes1 vol.

IllustratedYes

Dewey Decimal519.5

Table Of ContentIntroduction.- Statistical Learning.- Linear Regression.- Classification.- Resampling Methods.- Linear Model Selection and Regularization.- Moving Beyond Linearity.- Tree-Based Methods.- Support Vector Machines.- Deep Learning.- Survival Analysis and Censored data.- Unsupervised Learning.- Multiple Testing.- Index.

SynopsisAn 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.

LC Classification NumberQA276-280