LearnML - Mastering Machine Learning Algorithms

NOTE: The repository is currently under development and is not yet ready for use.

GitHub Repository: github.com/GopalSaraf/learnML
LearnML Website: learnML.gopalsaraf.com

Introduction

LearnML is a comprehensive collection of machine learning algorithms implemented from scratch using Python. Each algorithm is encapsulated as a class and thoroughly tested across different datasets.

The primary objective of this repository is to provide educational resources that facilitate a deep understanding of the internal mechanisms governing popular machine learning algorithms.

It is essential to emphasize that the algorithms included in this repository are intended for educational purposes and might not be optimized for production environments.

Installation

To get started with LearnML, follow these steps:

1. Clone the repository using the command: git clone https://github.com/GopalSaraf/learnML.
2. Move into the repository directory: cd learnML.
3. Create a virtual environment: python -m venv venv.
4. Activate the virtual environment:
   • On Linux/Mac: source venv/bin/activate
   • On Windows: venv\Scripts\activate
5. Install the required dependencies: pip install -r requirements.txt.
6. Install the LearnML library: pip install ..

Documentation

LearnML's documentation is generated using pdoc.

You can access the LearnML library documentation on the LearnML website.

For offline access, you can find the documentation in the docs folder. Check out the documentation here.

Examples

The examples folder contains practical examples that illustrate how to utilize the algorithms. Each example focuses on a specific algorithm and demonstrates its application on a sample dataset.

Contributing

Contributions to the LearnML repository are greatly appreciated! To contribute, follow these steps:

1. Fork the repository and create a new branch for your work.
2. Implement your changes and additions.
3. Validate your modifications using appropriate examples and ensure overall functionality.
4. Open a pull request, which will then be reviewed by the maintainers.

As previously mentioned, please be aware that the algorithms in this repository are meant for educational purposes and may not be optimized for production environments.

License

This repository is licensed under the MIT License.

Contact

If you have any inquiries or require assistance, feel free to contact Gopal Saraf.

Enjoy your learning journey!