The ordered TechClub path from beginner programming to advanced AI and robotics.
Curriculum overview
Follow the stages in order, but allow students to explore projects that match their interests. Each stage contains a recommended entry point, a completion check, and a project opportunity. Students should keep a notebook or README explaining what they tried and learned.
- Stage 1 — Foundations: Computing and Python and programming practice — run programs, debug failures, and build reusable code.
- Stage 2 — Data and mathematics: Data and mathematics — inspect, visualize, summarize, and question data.
- Stage 3 — Classical ML: Classical machine learning — train and evaluate baseline models.
- Stage 4 — Deep and generative AI: Deep learning, types of AI, and generative AI — train neural networks and build grounded applications.
- Stage 5 — Robotics and engineering: Robotics foundations, ROS 2 and autonomy, and engineering and safety — connect software to safe physical systems.
Before you begin
Read the student getting started guide. It explains prerequisites, setup, safe learning habits, and how to choose an entry point.
How to use the navigation
- Stage overview pages explain goals, prerequisites, and completion evidence.
- Lesson pages teach one concept with examples and practice.
- Project pages combine several lessons into a demonstrable result.
- Build and reference contains the rubric, glossary, troubleshooting, and primary sources.
Always open the stage overview before its lessons. Use the sidebar’s nested lesson list to continue in order.
Suggested pacing
| Stage | Typical time | Evidence of progress |
|---|---|---|
| Foundations | 4–8 weeks | Small Python programs and a documented notebook |
| Data and mathematics | 4–8 weeks | A data story with a chart and limitation |
| Classical ML | 4–8 weeks | Model comparison with an evaluation set |
| Deep and generative AI | 6–12 weeks | A tested model or grounded AI application |
| Robotics | 6–12 weeks | A safe simulated or physical robot behavior |
| Engineering and research | Ongoing | Reproducible project report and presentation |
Time depends on meeting frequency, student experience, and hardware access. Do not advance because a calendar says so; advance when the completion evidence is present.
Suggested progression
| Band | Recommended focus |
|---|---|
| Explorer | Foundations, Python, notebooks, and small games |
| Builder | Data, classical ML, and sensor projects |
| Inventor | Deep learning, generative AI, and robot autonomy |
| Researcher | Deployment, evaluation, safety, and independent projects |
How to know you are ready
- You can explain the current topic without copying its definition.
- Your code runs from a clean environment or a fresh notebook kernel.
- You tested at least one normal case and one failure case.
- You can name a limitation or possible misuse.
- You can show the work to another student and help them run it.