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.

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Image generated by AI

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Quickstart: Run on Codespaces or Locally

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

Run on GitHub Codespaces

Click Code > Open with Codespaces and start immediately!

Run Locally

1️⃣ Clone the repository:

git clone https://github.com/gozsari/ML-OneDay-Course.git
cd ML-OneDay-Course

2️⃣ Install uv (which is a lightweight alternative to virtualenv):

pip install uv

3️⃣ Create a virtual environment:

uv venv --python 3.12 (or any Python 3.11+ version)
source .venv/bin/activate

4️⃣ Install dependencies:

uv sync

5️⃣ Run Jupyter Notebook:

jupyter notebook

6️⃣ Open the Jupyter Notebook in your browser and start learning!

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📦 Dependencies

Package	Version
Python	3.11+
NumPy	latest
Pandas	latest
Scikit-learn	latest
Matplotlib	latest
Seaborn	latest
Jupyter	latest
joblib	latest

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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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