Step-by-Step Journey
🐣
1. Curious Kick-off
What is AI/ML? Where do we see it?
Learn
Try
Share
Graphical Visualisation
Training Curve
See accuracy go up and loss go down as the model learns! 📈
Model Skills Radar
A playful look at strengths after tuning.
Confusion Matrix
Pred: No
Pred: Yes
True: No
42
8
True: Yes
6
44
Total samples: 100
Try a Tiny Prediction
2 h/day
8 h/night
1 (0=none, 1=just right 🍎, 2=hungry 😅, 3=too many 🍪)
Adjust the sliders and tap Predict to see a playful result!
Cute Resources
Friendly API Checklist
- ✓ Input validation (Pydantic)
- ✓ Versioning (/v1, /v2)
- ✓ Auth (API keys / OAuth)
- ✓ Rate limits (e.g., slowapi)
- ✓ Tests (pytest + httpx)
- ✓ Monitoring (Prometheus, logs)
- ✓ CORS + timeouts + retries
AI Model Tips
- Keep it simple first
- Collect clean, diverse data
- Measure fairly (precision/recall)
- Explain decisions (feature importance)
- Watch data drift; re-train
Safety & Kindness
- Protect privacy
- Be bias-aware & inclusive
- Respect users & rate-limit
- Document clearly