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Introduction to Machine Learning: One-Day Course

A beginner-friendly one-day Machine Learning (ML) course covering fundamental concepts with hands-on examples.

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📌 Overview

This course introduces the basics of Supervised & Unsupervised Learning using Python and Scikit-learn.
You'll explore Regression, Classification, Clustering, Dimensionality Reduction, and Anomaly Detection through interactive Jupyter Notebooks.

📄 Slides: Presentation
📂 Notebooks: Course Materials
📘 Detailed Course Content: COURSE_CONTENT.md

This course has been prepared as part of the course "Introduction to Digital Resources" conducted by Chalmers e-Commons.

Quickstart: Run on using GitHub Codespaces or Marimo or Locally

You can run the course notebooks on GitHub Codespaces, Marimo, or locally on your machine.

Run on GitHub Codespaces

1️⃣ Click Code > Open with Codespaces and start immediately!

2️⃣ Open the Jupyter Notebook in your browser and start learning! They are located in the notebooks folder.

Run on Marimo (In Progress)

1️⃣ Create a virtual environment following the instructions in HOW_TO_CREATE_ENV.md

2️⃣ Run Marimo Book:
Quick start:

cd marimo_book
./start_book.sh

or for Windows:

cd marimo_book
start_book.bat

Then select Option 1 to open the interactive index.

marimo edit 0-Index.py

Run Locally

1️⃣ Create a virtual environment following the instructions in HOW_TO_CREATE_ENV.md

2️⃣ Run Jupyter Notebook:

jupyter notebook

3️⃣ Open the Jupyter Notebook in your browser and start learning!

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📦 Dependencies

Package	Version
Python	3.11+
NumPy	1.24.0
Pandas	2.0.0
Scikit-learn	1.3.0
Matplotlib	3.7.0
Seaborn	0.12.0
Jupyter	1.0.0
joblib	1.3.0
Marimo	0.17.0

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🔖 Citation

If you use this course, please cite it using the information in CITATION.cff.

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📜 License

This project is licensed under the MIT License.

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Acknowledgements

Special thanks to Leon Boschman for contributing ideas, slides, and feedback.

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