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How to Contribute

Thank you for your interest in contributing to the AI & ML Documentation project! This community-driven resource depends on contributions from people like you.

Ways to Contribute​

There are many ways to contribute to this project:

  • Add new content: Write new tutorials, guides, or reference documentation
  • Improve existing content: Fix typos, clarify explanations, update outdated information
  • Add code examples: Provide practical examples in Python, R, or other languages
  • Review and give feedback: Help review pull requests and provide constructive feedback
  • Report issues: Report bugs, suggest improvements, or request new content

Getting Started​

1. Set up your development environment​

  1. Fork the repository on GitHub
  2. Clone your fork locally
  3. Install dependencies with npm install
  4. Start the development server with npm start

2. Make your changes​

Our documentation is organized in the following directories:

  • /docs/machine-learning/: Machine Learning documentation
  • /docs/deep-learning/: Deep Learning documentation
  • /docs/language-models/: Language Models documentation
  • /docs/resources/: Resources, libraries, datasets, and tools

All documentation is written in Markdown with MDX extensions for interactive components.

3. Submit a pull request​

  1. Commit your changes to a new branch
  2. Push your branch to your fork
  3. Submit a pull request from your branch to the main repository
  4. Describe your changes in the pull request description

Content Guidelines​

Style​

  • Use clear, concise language
  • Break complex topics into digestible sections
  • Include diagrams and visualizations where helpful
  • Provide code examples with explanations
  • Link to relevant resources and references

Structure​

Each document should generally follow this structure:

  1. Introduction: Brief overview of the topic
  2. Main Content: Detailed explanation with sections and subsections
  3. Practical Examples: Code examples showing real-world usage
  4. Advanced Topics: More complex aspects (optional)
  5. Further Reading: Links to related documentation and external resources

Code Examples​

  • Include code examples in Python (preferred), R, or other relevant languages
  • Make sure code is runnable and produces the expected output
  • Explain the code thoroughly

Review Process​

All contributions go through a review process:

  1. Automated checks for formatting and basic errors
  2. Review by community members
  3. Review by project maintainers
  4. Merging by maintainers once approved

Community Guidelines​

We strive to maintain a welcoming and inclusive community. Please follow these guidelines in all interactions:

  • Be respectful and considerate
  • Focus on the content, not the person
  • Assume good intentions
  • Be open to feedback
  • Help others learn and grow

Questions?​

If you have any questions about contributing, please:

  • Open an issue on GitHub
  • Join our community Discord
  • Reach out to the maintainers

Thank you for helping make AI Seekhega India better for everyone!