Classical Machine Learning Algorithms

This repository is a collection of classical machine learning algorithms implemented in Python. It includes a variety of algorithms from different areas of machine learning, such as ensemble methods, reinforcement learning, semi-supervised learning, supervised learning, and unsupervised learning.

Table of Contents

• Ensemble
  • Bagging
  • Boosting
• Reinforcement Learning
  • Deep Q-Network (DQN)
  • Q-Learning
  • Temporal Difference (TD) Learning
• Semi-Supervised Learning
  • Label Propagation
  • Label Spreading
• Supervised Learning
  • Decision Trees
  • Huber Regressor
  • Lasso and Ridge Regression
  • Linear SVM
  • Linear Regression
  • Logistic Regression
  • Naive Bayes
  • Polynomial Regression
  • Random Forests
• Unsupervised Learning
  • Isolation Forest
  • K-Means Clustering
  • Principal Component Analysis (PCA)
  • t-SNE

Usage

Each folder in the repository contains a Python file corresponding to the algorithm mentioned. To use an algorithm, navigate to the respective folder and run the Python file.

Contributing

Contributions to this repository are welcome. Please feel free to fork the repo, add your contributions, and then make a pull request.

License

This project is open-sourced under the MIT license.