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.
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git clone [email protected]:coxy1989/ISL.git -
cd ISL -
conda env create -f environment.yml -
source activate isl -
jupyter notebook
- 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
[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