ML-Algo-Exploration

Description

The ML-Algo-Exploration project is an open-source initiative under the Mbarara University Biomedical Engineering Students Association (MBUBESA). This project provides a comprehensive platform with resources, tutorials, and community support to help users understand and implement machine learning models in the healthcare domain.

Mission: To empower individuals interested in learning machine learning algorithms for medical applications.

We focus on:

• Educational Advancement: Providing resources and tutorials for learners at all levels.
• Practical Application: Developing sustainable machine learning models for real-world medical challenges.
• Community Building: Fostering collaboration among students, researchers, and professionals.

How to Contribute

We welcome contributions from individuals at all skill levels.

• Review how to make contributions to this project here.

Review Process

1. Your pull request will be reviewed by project maintainers.
2. You may be asked to make additional changes or clarifications.
3. Once your pull request is approved, it will be merged into the main repository.

Contribution Guidelines

To ensure consistency and maintain the quality of the project, please follow these guidelines when contributing:

• Modularity: Write modular and reusable code. Each function or class should perform a single, well-defined task.
• Documentation: Properly document your code. Use docstrings to describe the purpose and usage of modules, classes, and functions. Follow the PEP 8 style guide for coding and documentation.
• Unit Testing: Include unit tests for your code. Tests should be placed in a tests subdirectory within the folder containing your code. Ensure your tests cover all critical functionality.
• Directory Structure: Organize your code in a single folder per algorithm. Each folder should contain:
  • The main code file(s).
  • A README.md file explaining the algorithm and how to use it.
  • A tests directory with unit tests.
• Code Review: All code will be reviewed before being added to the main branch. Ensure your code is clean, well-documented, and thoroughly tested.

Additional Information

For more in-depth guidance and additional resources, please visit our Resources repository/sub-folder.
You may find the following resources helpful too;

• Using Github: here
• Python Downloads: here
• Python 3.x Documentation: here

We're thrilled to welcome you to our community and eagerly anticipate your valuable contributions!

Conclusion

Let's create sustainable machine learning models for medical applications together.