Introduction to Statistical Learning

Linear Regression Analysis

Classification Methods

Resampling and Nonlinear Approaches

Ensemble Learning and Random Forests

Support Vector Machines and Model Selection

Unsupervised Learning Techniques

High-Dimensional Data Problems

Applications in R: Working on Real-world Datasets

Future Trends and Final Thoughts

An Introduction to Statistical Learning provides an overview of the concept of statistical learning, an essential toolset for making sense of vast and complex data sets. With a focus on applications in R, the book takes a practical approach to understanding statistical learning methods. It explores concepts like linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines and unsupervised learning.

non_fiction

science

An Introduction to Statistical Learning: with Applications in R