ml-recipes-py

Curated, well-documented machine learning recipes in Python.

Each recipe is a small, focused example (script or notebook) that shows one idea clearly, with simple, readable code.

Goals

• Make ML examples approachable for beginners and useful for intermediates.
• Prefer small, self-contained recipes over big frameworks.
• Encourage contributions: new recipes, better docs, small utilities.

Getting started

git clone https://github.com/cutewizzy11/ml-recipes-py.git
cd ml-recipes-py

python -m venv .venv
# Linux/macOS
source .venv/bin/activate
# Windows PowerShell
# .venv\\Scripts\\Activate.ps1

pip install .
# Or, for development
pip install .[dev]

Run tests:

pytest

Project structure

src/ml_recipes/
  datasets/
  models/
  training/
examples/notebooks/
docs/
tests/

Example recipes (initial)

• Linear regression with scikit-learn
• Iris classification with scikit-learn
• Linear regression from scratch (NumPy)

See examples/notebooks/ for Jupyter notebooks.

Contributing

• Read CONTRIBUTING.md.
• Check issues labeled good first issue and help wanted.
• Popular ways to contribute:
  • New notebooks covering classic ML tasks
  • Improving explanations and plots
  • Adding small utility functions in src/ml_recipes/

Code of Conduct

See CODE_OF_CONDUCT.md.