An Introduction to Statistical Learning
with Applications in Python
Series: Springer Texts in Statistics;
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36 127 Ft (34 407 Ft + 5% VAT)
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36 127 Ft
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Product details:
- Edition number 2023
- Publisher Springer International Publishing
- Date of Publication 2 July 2024
- Number of Volumes 1 pieces, Book
- ISBN 9783031391897
- Binding Paperback
- See also 9783031387463
- No. of pages607 pages
- Size 254x178 mm
- Language English
- Illustrations XV, 607 p. 600 illus., 575 illus. in color. Illustrations, black & white 803
Categories
Long description:
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.
MoreTable 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.
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