References

This section will be extended throughout the course. If you have good materials you want to share, please post them into our chat, and I will integrate them here.

Core Topic

• Google: Machine Learning Crash Course with TensorFlow APIs (~14h, interactive online course)

• FreeCodeCamp, Kylie Ying: Machine Learning for Everybody (~4h video)

• Sarah Ciston: A Critical Field Guide for Working with Machine Learning Datasets

Python Basics

• w3schools: Python Tutorial (Tutorial, well suited for beginners)

• FreeCodeCamp: Scientific Computing with Python (Full and extensive interactive online course)

• Al Sweigart: Automate the Boring Stuff with Python (A whole book on Python basics and what you can do with it to make your digital life easier - also freely accessible as a web page)

Official Python Documentation

• For an overview: Python 3.11.1 documentation

• As a first entry point: The Python Tutorial

• As a reference:

  • The Python Language Reference

  • The Python Standard Library

Tools

• Thonny - Python IDE for beginners

• PyCharm - more advanced IDE for developers

• Jupyter notebooks & more - web docs with interactive Python snippets (usable on your own device as a pip module, or on the jupyter.org site - Google’s Colab is also just a version of Jupyter notebooks)

Extended Materials

Extended materials on topics beyond what we will be doing in the course:

• FreeCodeCamp: Machine Learning with Python (Full and extensive interactive online course - would fit into our core course topic, but vastly exceeds it workload wise)

• Youtube, Computerphile: How AI Image Generators Work (Stable Diffusion / Dall-E) (~18min Video)

• Youtube, Computerphile: Stable Diffusion in Code (AI Image Generation) (~17min Video)