The book Introduction to Statistical Learning contains a wealth of information on machine learning algorithms, with labs and examples done in R, using public datasets. Each chapter contains exercises which are meant to be performed in R as well. Many, if not all, of the labs or exercises can be done in Python though.
There are some great GitHub repositories that have already ported code from some of the labs to Python and some of these repositories contain answers to the applied exercises in Python too, however not everything has been ported over yet. In particular, the second edition of ISLR was recently released (August 2021), which contain several new chapters not present in the first edition.
This repository aims to fill the void and contains Jupyter notebooks with code from the labs of these three chapters, which I've ported from R to Python. These notebooks try to reproduce the results found in the labs as closely as possible. Additionally, I've created Jupyter notebooks and used Python code to produce my own answers to the chapters' exercises.
- Chapter 10 - Deep Learning
- Chapter 11 - Survival Analysis and Censored Data
- Labs: Coming Soon
- Applied Exercises: Coming Soon
- Chapter 13 - Multiple Testing
- Labs: Comins Soon
- Applied Exercises: Coming Soon
As I work through other chapters in the book, I may also add any code that I create for those labs or exercises here too.
James, G., Witten, D., Hastie, T., Tibshirani, R. (2021). An Introduction to Statistical Learning with Applications in R, Second Edition, Springer Science+Business Media, New York. https://www.statlearning.com/