ISLP_learning

Welcome to the ISLP Exercise repository! This repository contains my hands-on exercises related to the book "Introduction to Statistical Learning with Python" concepts implemented in Python using Jupiter Notebooks.

Overview:

In this repository, you'll find practical implementations, and code snippets covering various statistical learning topics. The materials are organized into folders based on different concepts and techniques. Whether you're a beginner looking to understand the basics of statistical learning or an experienced data guy exploring basic topics, this repository aims to provide a comprehensive resource. 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. ISLR website

Folder Structure:

1. 02_Statistical_Learning: Statistical Learning
2. 03_Linear_Regression: Implementations and exercises related to linear regression.
3. 04_Classification: Implementations and exercises related to Classification.
4. 05_Resampling_Methods: Implementations and exercises related to Resampling Methods and algorithms.
5. 06_Linear_Model_Selection_and_Regularization: Implementations and exercises related to Linear Model Selection and Regularization.
6. 07_Moving_Beyond_Linearity: Implementations and exercises related to Moving Beyond Linearity
7. 08_Tree_Based_Methods: Implementations and exercises related to Tree-Based Methods
8. 09_Support_Vector_Machines: Implementations and exercises related to Support Vector Machines
9. 10_Deep_Learning: Implementations and exercises related to Deep Learning
10. 11_Survival_Analysis_and_Censored_Data: Implementations and exercises related to Survival Analysis and Censored Data
11. 12_Unsupervised_Learning: Implementations and exercises related to Unsupervised Learning
12. 13_Multiple_Testing: Implementations and exercises related to Multiple Testing

Feel free to explore the notebooks, experiment with the code, and adapt the exercises to learn the concepts.

Getting Started:

1. Clone the repository to your local machine.
2. Open the Jupyter Notebooks using your preferred environment.
3. Dive into each folder to explore different statistical learning topics.
4. Execute the code cells, tweak parameters, and observe the results.

I hope this repository serves as a valuable resource for your journey into statistical learning with Python. If you have any questions or suggestions, feel free to open an issue or contribute to make this repository even better!

Happy Learning! 🚀