ILSP Solutions
An Introduction to Statistical Learning with Applications in Python (ISLP) Solutions
The ISLP (Introduction to Statistical Learning), written by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, and Jonathan Taylor, is considered a gold standard, to use, for students pursuing prerequisites in machine learning. This book which is commonly found to be great in quality gets huge popularity as an introductory guide in the field of Machine Learning and Data Science. Click here to get PDF
The text covers mathematical and statistical theory of machine learning as well as applied labs in the programming language Python.
Below, you’ll find exercise solutions written in JupyterLab using Python and Markdown, hosted on GitHub, serving as a demonstration of learning and reinforcement of concepts.
Chapter 2: Statistical Learning
- Topics: What is statistical learning?, Basics of statistics
- Lab: Ch02-statlearn-lab
- Exercise: Ch02-2.4-Exercise
Chapter 3: Linear Regression
- Topics: Linear regression, Multi Regression
- Lab: Ch03-linreg-lab
- Exercise: Ch03-3.7-Exercise
Chapter 4: Classification
(Solutions not yet updated)
Chapter 5: Resampling Methods
(Solutions not yet updated)
Chapter 6: Linear Model Selection and Regularization
(Solutions not yet updated)
Chapter 7: Moving Beyond Linearity
(Solutions not yet updated)
Chapter 8: Tree-Based Methods
(Solutions not yet updated)
Chapter 9: Support Vector Machines
(Solutions not yet updated)
Chapter 10: Deep Learning
(Solutions not yet updated)
Chapter 11: Survival Analysis
(Solutions not yet updated)
Chapter 12: Unsupervised Learning
(Solutions not yet updated)
Chapter 13: Multiple Testing
(Solutions not yet updated)
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