Machine-Learning-Algorithms

This repository contains implementations of various machine learning algorithms from scratch in Python. It aims to provide a comprehensive resource for students, researchers, and practitioners to explore and understand the fundamentals of machine learning.

Purpose

The purpose of this project is to:

• Provide a collection of well-documented and easy-to-understand implementations of machine learning algorithms.
• Help individuals gain a deeper understanding of how machine learning algorithms work and how they can be applied to solve real-world problems.
• Encourage experimentation and exploration of different machine learning techniques.

Features

• Algorithms: The repository includes implementations of a wide range of machine learning algorithms, including:
  • Supervised Learning: Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines, K-Nearest Neighbors.
  • Unsupervised Learning: K-Means Clustering, Principal Component Analysis, Singular Value Decomposition.
  • Reinforcement Learning: Q-Learning, SARSA.
• Documentation: Each algorithm is accompanied by detailed documentation that explains the algorithm's intuition, mathematical formulation, and usage.
• Examples: The repository also includes Jupyter notebooks with examples demonstrating how to use the algorithms to solve real-world problems.

Technologies Used

• Python
• NumPy
• SciPy
• Matplotlib
• Seaborn
• Jupyter Notebooks

Getting Started

To get started with this project:

1. Clone the repository to your local machine:

 git clone https://github.com/singhsourav0/Machine-Learning-Algorithms.git

2. Install the required dependencies:

 pip install -r requirements.txt

3. Open a Jupyter notebook and navigate to the notebooks folder in the repository.

4. Open a notebook of your choice and follow the instructions to run the code and explore the algorithm.

Contribution Guidelines

Contributions to this project are welcome and encouraged. If you would like to contribute, please follow these guidelines:

• Fork the repository and create a new branch for your changes.
• Make sure your code is well-documented and follows the existing coding style.
• Add tests for new features or bug fixes.
• Submit a pull request with a detailed description of your changes.

License

This project is licensed under the MIT License.

Contact

For any questions, comments, or suggestions, please feel free to contact me at [email protected]