AI Code is an open-source initiative designed to make learning Artificial Intelligence (AI) more accessible, structured, and hands-on. Whether you're a beginner or an experienced developer, AI-Code provides scratch implementations of various AI algorithms alongside real-world project guides, helping you bridge the gap between theory and practice.
- Scratch-level implementations of AI algorithms 🧠
- Guides, datasets, research papers, and step-by-step tutorials 📘
- Clear directories with focused README files 📂
- Fast learning with minimal complexity 🚀
- Go through the Contributing Guidelines to fork and clone the project.
- After forking and cloning the project in your local system:
- Create a virtual environment:
python -m venv myenv
- Activate the virtual environment:
- On Windows:
myenv\Scripts\activate
- On macOS/Linux:
source myenv/bin/activate
- On Windows:
- Install the required Python package:
pip install mkdocs-material
- Create a virtual environment:
- After installing the package, run the following command to start the development server:
mkdocs serve
- Open the local server URL (usually
http://127.0.0.1:8000) in your browser. You are now ready to work on the project.
We want your work to be readable by others; therefore, we encourage you to note the following:
- File names should be in
kebab-caseletters (e.g.,music-genre-classification-model,insurance-cross-sell-prediction). - Follow the PROJECT README TEMPLATE and ALGORITHM README TEMPLATE for refrence.
- Do not upload images or video files directly. Use a GitHub raw URL in the documentation.
- Upload your notebook to Kaggle, make it public, and share the Kaggle embed link only. Other links are not accepted.
- Limit commits to 3-4 unless given permission by project Admins or Mentors.
- Keep commit messages clear and relevant; avoid unnecessary details.
- It must required to follow mentioned do/don't guidelines.
- Please fill the PR Template properly while making a Pull Request.
- Do not commit directly to the
mainbranch, or your PR will be instantly rejected. - Ensure all work is original and not copied from other sources.
- Add comments to your code wherever necessary for clarity.
- Include a working video and show integration with
AI-Code MkDocs Documentationwebsite as part of your PR. - For frontend updates, share screenshots and work samples before submitting a PR.
