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

This course introduces the basics ofSupervised & Unsupervised Learning using Python and Scikit-learn.
You'll exploreRegression, Classification, Clustering, Dimensionality Reduction, andAnomaly 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 byChalmers e-Commons.

Image generated by AI

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

ClickCode > Open with Codespaces and start immediately!

1️⃣ Clone the repository:

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

2️⃣ Create a virtual environment:

python3 -m venv .venvsource .venv/bin/activate

3️⃣ Install dependencies:

pip install -r requirements.txt

4️⃣ Run Jupyter Notebook:

jupyter notebook

5️⃣ Open the Jupyter Notebook in your browser and start learning!

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

This project is licensed under theMIT License.

Special thanks toLeon Boschman for contributing ideas, slides, and feedback.