Introduction to Machine Learning: One-Day Course

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

---

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

Image generated by AI

---

Quickstart: Run Locally or on Codespaces

Run Locally

1️⃣ Clone the repository:

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

2️⃣ Create a virtual environment:

python -m venv venv
source venv/bin/activate

3️⃣ Install dependencies:

pip install -r requirements.txt

4️⃣ Run Jupyter Notebook:

jupyter notebook

Run on GitHub Codespaces

Click Code > Open with Codespaces and start immediately!

---

📦 Dependencies

Package	Version
Python	3.8+
NumPy	latest
Pandas	latest
Scikit-learn	latest
Matplotlib	latest

---

🔖 Citation

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

---

📜 License

This project is licensed under the MIT License.

---

Acknowledgements

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