An Introduction to Statistical Learning: with Applications in Python | Agenda Bookshop
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A01=Daniela Witten
A01=Gareth James
A01=Jonathan Taylor
A01=Robert Tibshirani
A01=Trevor Hastie
Age Group_Uncategorized
Age Group_Uncategorized
Author_Daniela Witten
Author_Gareth James
Author_Jonathan Taylor
Author_Robert Tibshirani
Author_Trevor Hastie
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Category1=Non-Fiction
Category=PBT
Category=UFM
COP=Switzerland
Delivery_Delivery within 10-20 working days
Language_English
PA=Available
Price_€100 and above
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An Introduction to Statistical Learning: with Applications in Python

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.

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Original price €112.99
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A01=Daniela WittenA01=Gareth JamesA01=Jonathan TaylorA01=Robert TibshiraniA01=Trevor HastieAge Group_UncategorizedAuthor_Daniela WittenAuthor_Gareth JamesAuthor_Jonathan TaylorAuthor_Robert TibshiraniAuthor_Trevor Hastieautomatic-updateCategory1=Non-FictionCategory=PBTCategory=UFMCOP=SwitzerlandDelivery_Delivery within 10-20 working daysLanguage_EnglishPA=AvailablePrice_€100 and abovePS=Activesoftlaunch
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Product Details
  • Dimensions: 178 x 254mm
  • Publication Date: 01 Jul 2023
  • Publisher: Springer International Publishing AG
  • Publication City/Country: Switzerland
  • Language: English
  • ISBN13: 9783031387463

About Daniela WittenGareth JamesJonathan TaylorRobert TibshiraniTrevor Hastie

Gareth James is the John H. Harland Dean of Goizueta Business School at Emory University. He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. The conceptual framework for this book grew out of his MBA elective courses in this area. Daniela Witten is a professor of statistics and biostatistics and the Dorothy Gilford Endowed Chair at University of Washington. Her research focuses largely on statistical machine learning techniques for the analysis of complex messy and large-scale data with an emphasis on unsupervised learning. Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University and are co-authors of the successful textbook Elements of Statistical Learning. Hastie and Tibshirani developed generalized additive models and wrote a popular book with that title. Hastie co-developed much of the statistical modeling software and environment in R and invented principal curves and surfaces. Tibshirani invented the lasso and is co-author of the very successful book An Introduction to the Bootstrap. They are both elected members of the US National Academy of Sciences. Jonathan Taylor is a professor of statistics at Stanford University. His research focuses on selective inference and signal detection in structured noise.

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