Author: Leonardo Uieda
This is a very brief hands-on introduction to machine learning. It will cover some of the common nomenclature, principles, and applications.
The tutorial is in the form of a Jupyter notebook (tutorial.ipynb).
Here are some options for using it:
- Download the notebook and run it on your machine (preferred).
- Run it online on Binder which lets you try out the code and experiment but will not save your progress.
- View it online on nbviewer to read the text and look at the code but not run it.
- Is currently in their final year of a STEM undergraduate degree or early years of a postgraduate degree.
- Has studies the basics of statistics, Python programming, and linear algebra.
- Is interested in using machine learning in their projects or as a future career.
The tutorial is designed to be taught as a 1-2 hour session with live-coding. To do so, create a copy of the notebook and delete all or most of the code cells (it's OK to leave some in to allow more time in the tutorial).
Type in the code as you explain what you're doing. This will help you control your pacing and avoid going too fast. It also opens up the opportunity for you to make mistakes and teach students how to identify and solve them.
Ideally, have them follow along on their own computers, typing in the code with you. Make sure you also share a copy of the pre-filled notebook with students so that they can choose to not type and listen at the same time.
The original material for this tutorial can be found at leouieda/ml-intro. Comments, corrections, and additions are welcome.
All Python source code is made available under the BSD 3-clause license. You can freely use and modify the code, without warranty, so long as you provide attribution to the authors.
Unless otherwise specified, all figures and Jupyter notebooks are available under the Creative Commons Attribution 4.0 License (CC-BY).
The full text of these licenses is provided in the LICENSE.txt
file.