This workshop provides an introduction to machine learning using Python. Participants will use Scikit-Learn, NumPy and Matplotlib to solve problems using supervised and unsupervised machine learning techniques.
For more PICSciE/RC workshops on machine learning see the Fall 2024 program.
Click on a notebook above such as notebook1_regression.ipynb. After the notebook loads, click on the "Open in Colab" button. Run the cells. If you encounter the message "Warning: This notebook was not authored by Google" then choose "Run anyway".
- Point your browser to https://jdh4.github.io/python_machine_learning/
- After the JupyterLite interface loads, in the file browser on the left, double-click on a notebook such as
notebook1_regression.ipynb - Run the notebook as usual by clicking on the "play" button and so on.
The preferred web browsers are Firefox 90+ and Chromium 89+.
Mon, Jan 13 - Introduction to ML for the Humanities and Social Sciences (Part 1)
Tue, Jan 14 - Introduction to ML for the Humanities and Social Sciences (Part 2)
Wed, Jan 15 - Introduction to ML (Part 1)
Thu, Jan 16 - Introduction to ML (Part 2)
Fri, Jan 17 - Introduction to ML (Part 3)
Tue, Jan 21 - Introduction to ML (Part 4)
Wed, Jan 22 - Introduction to ML (Part 5: Computer Vision)
Wed, Jan 22 - Introduction to ML (Part 5: ML for Physical Simulations)
Wed, Jan 22 - Introduction to ML (Part 5: LLMs [Part 1 of 2])
Thu, Jan 23 - Introduction to ML (Part 5: LLMs [Part 2 of 2])
If you encounter any difficulties with the material in this guide then please attend a help session.
This guide was created by Christina Peters with contributions from Julian Gold and Jonathan Halverson.