A project-first learning path for school students exploring programming, AI, machine learning, and robotics.
Learn by building
TechClub helps students move from their first Python program to responsible AI and robotics projects. Every lesson will combine a short explanation, a runnable notebook, practice, and a project.
Note: The foundations, data, classical ML, and core AI-system lessons are available now. Advanced deep-learning, generative-AI, robotics, and research pages are marked as outlines where hands-on lessons are still being expanded.
Start here
- Read the learning path overview.
- Begin with computing and Python foundations.
- Use the Jupyter notebook workflow for experiments.
- Choose a project from the project studio.
The path
| Stage | What students learn |
|---|---|
| Foundations | Computing, Python, Git, notebooks, and problem solving |
| Data and mathematics | Data literacy, statistics, visualization, and linear algebra |
| Machine learning | Classical ML, evaluation, and responsible experimentation |
| Deep and generative AI | Neural networks, vision, language models, and RAG |
| Robotics | Electronics, sensors, control, ROS 2, and autonomy |
| Engineering | Deployment, safety, research, and portfolio projects |
Our learning principles
- Build small things before studying large systems.
- Explain every result, including mistakes and uncertainty.
- Use public, documented references and credit them.
- Protect privacy and keep people in control of consequential decisions.
- Work in teams, review code, and share what you learned.