With the exponential growth of data collection across diverse fields, statistical learning has emerged as an indispensable toolkit for comprehending data trends. “An Introduction to Statistical Learning” offers a comprehensive yet accessible exploration of key concepts in statistical learning, catering to a wide audience interested in contemporary data analysis tools.
Originally released in 2013 with applications in R (ISLR), the second edition published in 2021 has expanded its reach, now available in multiple languages including Chinese, Italian, Japanese, Korean, Mongolian, Russian, and Vietnamese. Additionally, the Python edition (ISLP) was released in 2023, ensuring accessibility across different programming languages.
Each edition features practical lab exercises at the end of chapters, allowing readers to apply concepts using either R or Python.
The chapters cover the following topics:
- 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
Source and reference to read the book – https://www.statlearning.com/
