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Learn statistical learning with applications in Python from the authors of the popular R book. This book covers topics such as regression, classification, resampling, tree-based methods, deep learning, and more.
A book that covers the essential statistical learning methods and applications with Python labs. It is a Python-based alternative to the popular R-based book An Introduction to Statistical Learning by the same authors.
This book focuses on theoretical aspects of Statistical Learning and Sequential Prediction, a unified approach to analyzing learning in both scenarios, brings together ideas from probability and statistics, game theory, algorithms, and optimization.
Presents an essential statistical learning toolkit for practitioners in science, industry, and other fields; Demonstrates application of the statistical learning methods in R; Includes new chapters on deep learning, survival analysis, and multiple testing
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.
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An Introduction to Statistical Learning: with Applications in Python - Ebook written by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor. Read this book using...
The course for An Introduction to Statistical Learning, with Applications in Python is available here. The video lectures covering the chapter material are the same for both courses. The courses also include sessions in R/Python, which differ between the two courses.
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