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An Introduction To Statistical Learning With Applications In Python 1st Gareth James

  • SKU: BELL-50703060
An Introduction To Statistical Learning With Applications In Python 1st Gareth James
$ 31.00 $ 45.00 (-31%)

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An Introduction To Statistical Learning With Applications In Python 1st Gareth James instant download after payment.

Publisher: Springer
File Extension: PDF
File size: 12.59 MB
Pages: 617
Author: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor
ISBN: 9783031387463, 9783031387470, 9783031391897, 3031391896, 3031387465, 3031387473, 1431875X
Language: English
Year: 2023
Edition: 1st

Product desciption

An Introduction To Statistical Learning With Applications In Python 1st Gareth James by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor 9783031387463, 9783031387470, 9783031391897, 3031391896, 3031387465, 3031387473, 1431875X instant download after payment.

An Introduction to Statistical Learning, With Applications in R (ISLR)
— first published in 2013, with a second edition in 2021 — arose from
the clear need for a broader and less technical treatment of the key topics
in statistical learning. In addition to a review of linear regression, ISLR
covers many of today’s most important statistical and machine learning
approaches, including resampling, sparse methods for classification and regression,
generalized additive models, tree-based methods, support vector
machines, deep learning, survival analysis, clustering, and multiple testing.
In recent years Python has become an increasingly popular language
for data science, and there has been increasing demand for a PythonLearning, With Applications in Python (ISLP), covers the same materials
as ISLR but with labs implemented in Python — a feat accomplished by the
addition of a new co-author, Jonathan Taylor. Several of the labs make use
of the ISLP Python package, which we have written to facilitate carrying out
the statistical learning methods covered in each chapter in Python. These
labs will be useful both for Python novices, as well as experienced users.
The intention behind ISLP (and ISLR) is to concentrate more on the
applications of the methods and less on the mathematical details, so it is
appropriate for advanced undergraduates or master’s students in statistics
or related quantitative fields, or for individuals in other disciplines who
wish to use statistical learning tools to analyze their data. It can be used
as a textbook for a course spanning two semesters.

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