Machine Learning
Implementation-first guide with clear tasks and evidence requirements.
Practical Tasks
- Train baseline and document assumptions
- Improve model through tuning/feature work
- Compare metrics and explain tradeoffs
Evidence to Publish
- Screenshots/metrics showing completion
- Short README note for approach and tradeoffs
- Known limitations and next improvements
Review Checklist
- Task output is reproducible
- Edge cases were tested
- Repository structure is clean