ISL

An Introduction to Statistical Learning with applications in R [1] is a classic textbook written by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. This repository contains my solutions to the labs and exercises, I follow in Python rather than R, with heavy use of: numpy, pandas, sklearn, matplotlib, seaborn, patsy and statsmodels.

Quickstart

1. git clone [email protected]:coxy1989/ISL.git

2. cd ISL

3. conda env create -f environment.yml

4. source activate isl

5. jupyter notebook

Run the notebooks

• Chapter 2: Statistical Learning: Conceptual
• Bonus: The Curse of Dimensionality
• Chapter 3: Linear Regression: Conceptual
• Chapter 3: Linear Regression: Applied
• Chapter 4: Classification: Conceptual
• Chapter 4: Classification: Applied
• Chapter 4: Classification: Lab
• Chapter 5: Resampling Methods: Conceptual
• Chapter 5: Resampling Methods: Applied
• Chapter 5: Resampling Methods: Lab
• Bonus: The Mean of Correlated Quatities
• Chapter 6: Linear Model Selection & Regularization: Conceptual
• Chapter 6: Linear Model Selection & Regularization: Applied
• Chapter 6: Linear Model Selection & Regularization: Lab
• Chapter 7: Moving Beyond Linearity: Conceptual
• Chapter 7: Moving Beyond Linearity: Applied
• Chapter 7: Moving Beyond Linearity: Lab
• Chapter 8: Tree-Based Methods: Conceptual
• Chapter 8: Tree-Based Methods: Applied
• Chapter 8: Tree-Based Methods: Lab
• Chapter 9: Support Vector Machines: Conceptual
• Chapter 9: Support Vector Machines: Applied
• Chapter 9: Support Vector Machines: Lab
• Chapter 10: Unsupervised Learning: Conceptual
• Chapter 10: Unsupervised Learning: Applied
• Chapter 10: Unsupervised Learning: Lab

References

[1] Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. An Introduction to Statistical Learning with Applications in R. New York Springer, 2013.

This repository was split out from mlsabattical on 09/01/2018