An Introduction to Statistical Learning with Applications in R
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
An overview of An Introduction to Statistical Learning
"An Introduction to Statistical Learning with Applications in R" is a textbook that provides an overview of statistical learning and its applications in R, a popular programming language used for data analysis and statistical computing. The book covers the following topics:
- The basics of statistical learning and its importance in real-world applications
- Overview of supervised and unsupervised learning methods
- Model evaluation and selection
- Regression and classification algorithms
- Resampling methods, including cross-validation
- Tree-based methods and random forests
- Support Vector Machines (SVMs)
- Unsupervised learning techniques such as clustering and dimension reduction
The book is intended for students, researchers, and practitioners who want to learn about statistical learning and its applications in R. The textbook is designed to provide a comprehensive introduction to the subject and includes numerous examples and exercises to reinforce the concepts presented. The use of R allows users to implement the statistical learning algorithms discussed in the book, making it a valuable resource for individuals who want to gain hands-on experience with statistical learning.
| Description : | Download An Introduction to Statistical Learning with Applications in R course, PDF ebook on 612 pages. |
| Level : | Advanced |
| Created : | November 8, 2021 |
| Size : | 13.81 MB |
| File type : | |
| Pages : | 612 |
| Author : | Gareth James • Daniela Witten • Trevor Hastie • Robert Tibshirani |
| Licence : | Creative commons |
| Downloads: | 1695 |
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