An introduction to statistical learning : with applications in Python / Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor.
2023
QA276
Linked e-resources
Linked Resource
Online Access
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks
Details
Title
An introduction to statistical learning : with applications in Python / Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor.
ISBN
9783031387470 (electronic bk.)
3031387473 (electronic bk.)
9783031387463
3031387465
3031387473 (electronic bk.)
9783031387463
3031387465
Published
Cham : Springer, 2023.
Language
English
Description
1 online resource (xv, 60 pages) : illustrations (some color).
Other Standard Identifiers
10.1007/978-3-031-38747-0 doi
Call Number
QA276
Dewey Decimal Classification
519.5
Summary
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.
Note
Includes index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed July 5, 2023).
Added Author
Witten, Daniela, author.
Hastie, Trevor, author.
Tibshirani, Robert, author.
Taylor, Jonathan, author.
Hastie, Trevor, author.
Tibshirani, Robert, author.
Taylor, Jonathan, author.
Series
Springer texts in statistics, 2197-4136
Available in Other Form
Print version: 9783031387463
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
All Resources
All Resources
Table of Contents
Introduction
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.
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.