Picture 1 of 4

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 (2023, Hardcover)

hzhmeng (574)

100% positive Feedback

Price:

US $65.00

Approximately£48.93

+ $29.59 postage

Estimated delivery Mon, 2 Jun - Tue, 10 Jun

Returns:

30 days return. Buyer pays for return postage. If you use an eBay delivery label, it will be deducted from your refund amount.

Condition:

New

About this product

Product Identifiers

PublisherSpringer International Publishing A&G

ISBN-103031387465

ISBN-139783031387463

eBay Product ID (ePID)18061465611

Product Key Features

Number of PagesXv, 60 Pages

Publication NameIntroduction to Statistical Learning : with Applications in Python

LanguageEnglish

Publication Year2023

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

TypeTextbook

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

Subject AreaMathematics, Computers

SeriesSpringer Texts in Statistics Ser.

FormatHardcover

Dimensions

Item Weight52.8 Oz

Item Length10 in

Item Width7 in

Additional Product Features

Dewey Edition23

TitleLeadingAn

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