ML-Algorithms-Hub

A comprehensive collection of Machine Learning algorithms, showcasing implementations in Python for supervised, unsupervised, and reinforcement learning techniques. This repository is designed for anyone looking to learn, experiment, or reference core machine learning concepts.

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Features

• Supervised Learning
  • Regression Algorithms (e.g., Linear Regression, Support Vector Regression, Polynomial Regression)
  • Classification Algorithms (e.g., Logistic Regression, SVM, KNN)
• Unsupervised Learning (coming soon!)
  • Clustering (e.g., K-Means, DBSCAN)
  • Dimensionality Reduction (e.g., PCA)
• Reinforcement Learning (planned!)
  • Value-Based Methods (e.g., Q-Learning)
  • Policy-Based Methods (e.g., DDPG)

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Getting Started

1. Clone the repository:

   git clone https://github.com/philippogol/ML-From-Regression-to-Reinforcement.git
   cd From-Regression-to-Reinforcement

2. Virtual Environment Setup:

   • Create a virtual environment:

     python3 -m venv venv

   • Activate the virtual environment:

     source venv/bin/activate

3. Install Required Libraries:

   pip install -r requirements.txt

4. Run Examples:

   • Navigate to the desired folder or script and execute:

     python3 regression/linear_regression.py
     python3 regression/support_vector_regression.py
     python3 regression/polynomial_regression.py

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Contribution

Contributions are welcome! Feel free to open a pull request or submit an issue.

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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