Machine Learning
Implementation
This folder contains notebooks with practical walkthroughs, exercises and solutions. It is recommended to use Google Colab to run and edit these notebooks. This is free to use, but requires linking to a google account. In Colab you can open the notebooks directly from a github repository, but to save progress its easiest to create a copy in Google Drive and work from that copy. Alternatively you can run the notebooks locally (via JupyterLab or Jupyter Notebook) if you have all the dependencies installed.
You can use github to version control your changes if you'd like, but please don't push changes to this repository. Instead make sure you fork this repository to your own github account, and then work with your fork.
Lectures
This folder contains lecture slides and some non-coding exercises. Unfortunately I no longer have access to the lecture recordings, so the slides might not all make perfect sense without the lecture recordings to accompany them. Please feel free to ask me any questions if you're unsure about anything covered in the lecture slides!
Coursework
This folder contains instructions for a longer exercise than the ones found in the Implementation folder. Hopefully it's self-explanatory, but again please get in touch if anything is unclear.