Session by session
- Session 11 · Ask & understandWeek 6
Read CSV values and labels
Evidence: a question, sketch or new vocabulary
- Session 12 · Build & designWeek 6
Separate features, labels and held-out rows
Evidence: a design draft, dataset or build
- Session 13 · ImplementWeek 7
Build a simple threshold baseline and model scaffold
Evidence: a working version, explained once
- Session 14 · Test & improveWeek 7
Evaluate unseen conditions
Evidence: a test log with at least one failure
- Session 15 · Explain & reflectWeek 8
Explain which result is stronger and why
Evidence: an individual explanation
- Local Python/scikit-learn
- prepared or P01 CSV
Held-out test rows stay out of fitting; report class counts.
A model, an evaluation study or an AI-checking workflow.
Completion needs the artefact, an honest test log, an individual explanation and no open safety or privacy issue.
Projects in the same block
- RoboticsG09-P01
Analog Measurement Lab
A light-measurement rig and a labelled dataset.
Sessions 01-05All plans - RoboticsG09-P02
Servo Position Bench
A one-servo positioning rig with calibrated limits.
Sessions 06-10All plans - AIG09-P03
Python Light Classifier
A simple light-state model compared with a transparent rule.
Sessions 11-15All plans - IntegratedG09-P04
AI Shade Adviser
A supervised shade-control model using a laptop prediction.
Sessions 16-20All plans
For school leadersChoose a starting point.
Build from evidence.
Pick the classes and a plan. We map the timetable, kit and safety checks with you, then pilot one class first.
- Prospectus and class-wise plan
- Kit and readiness check
- Pilot one class first
- Evidence at every milestone
Let's plan your pilot.
Share a few details and we will send the right plan for your classes.