An Introduction to Statistical Learning with Applications in Python

An Introduction to Statistical Learning with Applications in Python provides a clear, accessible, and less technical overview of the core ideas in statistical learning. Designed for anyone interested in applying modern data analysis tools, the book uses intuitive explanations, full-color graphics, and real-world examples to bring concepts to life. It is suitable for both statisticians and non-statisticians who want to understand and apply state-of-the-art statistical learning methods to real data using Python.

Table of Contents

• What is statistical learning?
• Regression
• Classification
• Resampling methods
• Linear model selection and regularization
• Moving beyond linearity
• Tree-based methods
• Support vector machines
• Deep learning
• Survival analysis
• Unsupervised learning
• Multiple testing

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