👋 python_machine_learning_course

🪧 Description

This project is a doc-based repository full of markdowns and jupyter notebooks rendered using mkdocs mkdocs-material and mkdocs-jupyter that will teach you about python basics focused to Machine Learning 😎

• Python Basics
• Set up a python project
• Numpy
• Pandas
• Matplotlib and Seaborn
• Scikit-Learn

👇 Web Link 👇

https://matesanz.github.io/python-machine-learning-course/

🗃️ Table of Contents

• 👋 python_machine_learning_course
  • 🪧 Description
  • 🗃️ Table of Contents
    • 📚 Documentation
  • 🏗️ Contributing
    • 🐋 Devcontainer Environment
    • 🧑‍⚖️ Pre-Commit
  • 🙋 Author

📚 Documentation

To launch documentation:

mkdocs serve

👉 Then go to http://localhost:8000

🏗️ Contributing

Fastest way to contribute is to use the devcontainer environment and the pre-commit hooks.

🐋 Devcontainer Environment

It is possible to have a development environment up an ready using Docker and vscode:

1. Install remote containers in VSCode.

   1. Press Ctrl+P
   2. Paste ext install ms-vscode-remote.remote-containers
   3. Press Enter

2. Run the docker in development in VSCode (wait, first time takes some time to run) :

   F1 > Open Folder in Container
   Select the desired folder (backend, frontend...)

It automatically searches for de .devcontainer/devcontainer.json file in the root folder. To apply changes made to the dockerfile or the devcontainer.json:

F1 > Rebuild Container

👍 It will install automatically python_ml_course in development mode and all the pre-commit hooks along all the tools needed for a correct development: black, isort, pylint, mypy, pytest...

🧑‍⚖️ Pre-Commit

In order to keep code and commits quality we enforce the use of pre-commit by doing:

pre-commit install

This will install a bunch of hooks that will check staged files (only the *.py staged files) to check that they stick to black, autopep8, isort and some other standards.

🙋 Author

🙋 Name: Andrés Matesanz

📩 Email: Matesanz.Cuadrado@gmail.com