• About This Course
• Key Features
• Prerequisites
• Course Structure
• Weekly Schedule
• Lab Notebooks
• Getting Started
• Recommended Resources
• Instructor

This is a 12-week intensive summer school on Machine Learning, designed for students with some Python experience. Each session is 2 hours: 1 hour of lecture followed by 1 hour of hands-on lab in Google Colab.

The course takes a practical, code-first approach — every concept is immediately reinforced with working scikit-learn code. By the end, students can build, evaluate, and reason about a wide range of ML models.

• 📚 12 lecture decks — covers the full supervised + unsupervised ML landscape
• 💻 12 Colab-ready lab notebooks — Guided → Fill-in-the-blank → Challenge structure
• 🔬 Real datasets — MNIST, California Housing, Iris, Breast Cancer, and more
• 🧪 Consistent pedagogy — every lab has the same 3-part format for predictable pacing
• 📖 Textbook-aligned — all slides reference Géron's Hands-On ML (3rd ed.)

Students should be comfortable reading Python code. No prior ML or deep learning experience is assumed.

Format: 2 hours/week · 1hr lecture + 1hr lab · 12 weeks

In this initial part, we’ll guide you through the essential tools needed for data science and machine learning, including installing VSCode, Jupyter Notebooks, Python virtual environments, Git for version control, GitHub for collaboration, Markdown, and creating a Medium blog post.

Every lab follows the same structure to make pacing predictable:

All notebooks are designed for Google Colab — no local setup required. Click any badge below to open directly:

Lab	Topic	Open in Colab
01	Python, NumPy & Pandas Warmup
02	California Housing EDA
03	Preprocessing & Pipelines
04	Binary Classification on MNIST
05	Multiclass Classification & Error Analysis
06	Training Models & Gradient Descent
07	Regularization & Logistic Regression
08	Support Vector Machines
09	Decision Trees
10	Ensemble Learning & Random Forests
11	Neural Networks with Keras
12	Unsupervised Learning — Clustering

No setup needed. Click any "Open in Colab" badge above. All required packages (scikit-learn, TensorFlow/Keras, matplotlib, pandas) are pre-installed in Colab.

💡 For Lab 11 (Neural Networks), go to Runtime → Change runtime type → T4 GPU for faster training.

# Clone the repository
git clone https://github.com/YOUR_USERNAME/YOUR_REPO.git
cd YOUR_REPO

# Create a virtual environment
python -m venv ml-env
source ml-env/bin/activate       # macOS/Linux
ml-env\Scripts\activate          # Windows

# Install dependencies
pip install -r requirements.txt

# Launch Jupyter
jupyter notebook labs/

requirements.txt:

numpy>=1.24
pandas>=2.0
matplotlib>=3.7
scikit-learn>=1.3
tensorflow>=2.13
jupyter

• 📘 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (3rd ed.) — Aurélien Géron All slides in this course are based on this book.

• 📗 Python for Data Analysis — Wes McKinney (free online)
• 📙 Python Data Science Handbook — Jake VanderPlas (free online)

• 🎥 YouTube series on Hands-On ML book chapters

• 🏆 Kaggle — competitions and free courses
• 💡 Google ML Crash Course

Found a bug in a notebook? Have a suggestion for a challenge problem? Pull requests are welcome.

1. Fork the repository
2. Create a feature branch (git checkout -b fix/lab-03-typo)
3. Commit your changes
4. Open a pull request

This course material is released under the MIT License. The slides are based on content from Hands-On Machine Learning by Aurélien Géron (O'Reilly Media) — please respect the original work's copyright when sharing.