It's a 1 hour introduction to supervised and unsupervised machine learning, giving you examples of what they do and an intuition as to how they work.
This isn't a comprehensive introduction to the topic, and is by no means a substitute for going through the materials of an undergraduate or graduate level course (either on your own or actually taking a class).
To learn more about this topic, here are some publicly available notes from an undergraduate course at U of T.
We have two modules for this workshop (url corresponds to notebook for given module):
- Supervised Learning (https://github.com/whitleyo/STEM_Fellowship_BDC_ML_workshop/blob/master/supervised_learning.ipynb)
- Unsupervised Learning (https://github.com/whitleyo/STEM_Fellowship_BDC_ML_workshop/blob/master/unsupervised_learning.ipynb)
To run through the workshop modules, we'll use Google Colab (colab.research.google.com) which provides free computing resources without the hassle of setting up an environment to run python and use commonly used libraries. More info here.
Instructions for running each module:
- set up a google account (you already have one if you have a gmail account) if you don't have one already
- go to colab.research.google.com after signing into your google account.
- you should be redirected to a page like this:
Click on File -> Open Notebook. You should now see a menu like this:
-
Click on GitHub, and enter the url of the notebook. For the Supervised Learning module, click on supervised_learning.ipynb. For the Unsupervised Learning module, click on unsupervised_learning.ipynb
-
Following along with the instructor, you can run each code cell. Note that before you run a given cell, you should have run all previous cells beforehand so that functions/variables/classes are declared and modules are imported.
Credits:
Supervised Learning Module: Chloe Zhu, Edits by Owen Whitley
Unuspervised Learning Module: Owen Whitley