ML-Regression-Notebooks

This repository houses a collection of Jupyter notebooks breaking down a variety of regression algorithms, and exploring different approaches within each model.

Notebook Categories

The following notebooks can be found in the notebooks/ directory:

• linear_regression.ipynb: Breaking down linear regression
• gradient_descents.ipynb: Batch, Stochastic, Mini-batch gradient descents and exploring learning curves
• polynomial_regression.ipynb: Breaking down polynomial linear regression
• regularizing_linear_models.ipynb: Ridge, Lasso, Elastic Net and Early Stopping techniques
• logistic_regression.ipynb: Breaking down logistic regression

Getting Started

1. Clone the repository to your local machine:

   git clone https://github.com/Zane-dev16/ML-Regression-Notebooks.git

2. Run the notebooks in your preferred order on any compatible platform

Bibliography

• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (3rd Edition) by Aurélien Géron