How to Contribute Pull Requests to the Microsoft ML-For-Beginners Repository

To contribute a pull request to Microsoft's ML-For-Beginners repository, fork the repo, create a feature branch, set up the Conda environment defined in environment.yml, edit the Jupyter notebooks or Markdown files in the chapters/ directory, verify execution with jupyter lab, and submit a PR using the template in .github/PULL_REQUEST_TEMPLATE.md while adhering to the guidelines in CONTRIBUTING.md.

The ML-For-Beginners repository by Microsoft is an open-source collection of Jupyter notebooks and tutorials designed to teach core machine-learning concepts to beginners. Contributing pull requests to this Microsoft repo follows a structured workflow that ensures educational content remains accurate, executable, and consistent with the project's learning path. All contributions must comply with the standards documented in the repository's root-level configuration files.

Understand the Repository Structure

Before submitting changes, familiarize yourself with the organization of the ML-For-Beginners codebase. The repository separates content, configuration, and automation into distinct directories.

The chapters/ Directory

The chapters/ folder contains the primary educational content. Each subdirectory represents a tutorial chapter (e.g., chapter1_introduction) and holds both a Jupyter notebook (*.ipynb) and a companion Markdown file. When you contribute pull requests to add or modify lessons, you edit files within this directory structure.

Configuration and Templates

The .github/ directory houses GitHub-specific configurations, including the PULL_REQUEST_TEMPLATE.md that pre-populates your PR description with required fields. The docs/ directory contains static site assets for GitHub Pages deployment, while environment.yml in the root defines the Conda environment required to run notebooks reproducibly.

Step-by-Step Contribution Workflow

Follow this standardized open-source workflow to ensure your contribution meets Microsoft's quality standards and passes automated CI checks.

1. Fork and Clone the Repository

Create your own copy of the repository by clicking the Fork button on the GitHub page. Clone your fork locally to begin development:

git clone https://github.com/<your-username>/ML-For-Beginners.git
cd ML-For-Beginners

2. Create a Feature Branch

Isolate your changes on a short-lived branch scoped to a single feature or fix. This simplifies review and maintains a clean commit history:

git checkout -b <feature-name>

Use descriptive names like add-regression-example or fix-chapter3-typo to indicate the branch's purpose.

3. Configure the Development Environment

The repository uses a Conda environment defined in environment.yml to ensure all notebooks run without missing dependencies. Set up your environment once per clone:

conda env create -f environment.yml
conda activate ml4b

This creates an isolated Python environment with the specific package versions tested by the maintainers.

4. Edit Notebooks and Documentation

Make your changes to the appropriate files within chapters/. Follow the style guidelines documented in CONTRIBUTING.md: use clear hierarchical headings, keep code cells small and focused, and add explanatory comments for complex logic. If your changes alter a tutorial's scope, update the corresponding section in the main README.md to maintain coherence across the learning path.

5. Validate Your Changes Locally

Execute notebooks from start to finish to ensure they run without errors. Launch Jupyter and manually verify execution:

jupyter lab

Some chapters include unit tests under tests/. Run these with pytest to catch regressions:

pytest tests/

Local verification prevents CI failures when you submit your pull request to the Microsoft repository.

6. Commit and Push

Stage your changes and commit using the conventional commit format. Meaningful messages help maintainers understand your contribution at a glance:

git add .
git commit -m "feat: add classification example notebook"
git push origin <feature-name>

The commit message automatically appears in the PR description when you open the request.

7. Open and Complete the Pull Request

Navigate to the original microsoft/ML-For-Beginners repository and click New Pull Request. Select your branch as the source. The PR template in .github/PULL_REQUEST_TEMPLATE.md will pre-populate fields such as "Why is this change needed?" and "Testing steps". Complete these sections clearly.

Address any feedback from reviewers by amending your branch. New commits pushed to your fork update the PR automatically.

Review Criteria and Merge Requirements

Microsoft maintainers evaluate contributions against automated and manual criteria before merging. All notebooks must execute cleanly on the CI workflow defined in .github/workflows/. The CI pipeline checks for unsafe imports, validates SPDX license headers, and ensures notebooks run without errors.

You must sign the Microsoft Contributor License Agreement (CLA) when prompted by the PR interface. Documentation updates must preserve the logical flow of the learning path. Once automated checks pass and reviewers approve, a maintainer merges the PR, and GitHub Pages automatically publishes the updated tutorials.

Summary

• Fork and branch: Create a personal fork and use short-lived feature branches like add-regression-example to isolate changes.
• Environment setup: Use conda env create -f environment.yml and conda activate ml4b to match the repository's runtime dependencies.
• Edit in chapters/: Modify Jupyter notebooks and Markdown files, following the style guide in CONTRIBUTING.md.
• Local verification: Run notebooks with jupyter lab and execute pytest tests/ to prevent CI failures.
• PR requirements: Fill out the template in .github/PULL_REQUEST_TEMPLATE.md, sign the Microsoft CLA, and ensure all GitHub Actions checks pass.

Frequently Asked Questions

Do I need to sign a CLA to contribute to Microsoft ML-For-Beginners?

Yes. Microsoft requires all contributors to sign the Contributor License Agreement (CLA) before merging any pull request. The CLA bot will prompt you to sign electronically when you open your first PR. This agreement grants Microsoft the necessary rights to use your contribution while ensuring you retain copyright ownership.

What files should I edit when adding a new tutorial?

Create a new subdirectory under chapters/ using the naming convention chapterN_topic (e.g., chapter4_regression). Place your Jupyter notebook (*.ipynb) and a corresponding Markdown file inside this folder. If your tutorial introduces new dependencies, update environment.yml in the root directory and document the change in your PR description.

How do I test my changes before submitting a PR?

Activate the Conda environment with conda activate ml4b, then launch Jupyter Lab using jupyter lab and execute all cells in your modified notebooks sequentially. For chapters with test coverage, run pytest tests/ from the repository root. Verify that no exceptions occur and that outputs match expected results before pushing your branch.

What should I include in my pull request description?

The repository provides a template in .github/PULL_REQUEST_TEMPLATE.md that automatically populates your PR description. Fill out the required fields explaining why the change is needed, what specific modifications you made, and the steps you took to test the changes. Reference any related issues and confirm that you have read CONTRIBUTING.md and CODE_OF_CONDUCT.md.

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