Machine Learning Essentials
Practical ML for regulated environments — with an emphasis on validation and explainability.
Outcomes
- Understand supervised vs. unsupervised methods
- Evaluate models correctly (leakage, drift, stability)
- Explain and document results for audit
Outline (1–2 days)
- Problem framing and data readiness
- Linear/logistic regression and decision trees
- Validation, cross‑validation and backtesting
- Monitoring, documentation and model risk
- Ethics, privacy and bias
Audience & tooling
Data‑curious teams looking to adopt ML responsibly. Tools: Python/R or no‑code demos.