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Machine Learning Foundations (Python)
A practical, end-to-end introduction to supervised ML: data prep, modeling, evaluation, and production-ready baselines.
MLPythonscikit-learnMetricsPipelines
Duration
3 days
Format
Remote
Level
Beginner
Key outcomes
- Frame problems correctly (regression vs classification) and avoid leakage
- Build preprocessing + modeling pipelines with scikit-learn
- Evaluate models with the right metrics and error analysis
- Deliver a reproducible baseline ready for API integration
Syllabus
Day 1 — From data to features
- Problem framing and dataset split strategy
- Leakage pitfalls and validation mindset
- Missing values, encoding, scaling
- Pipelines: ColumnTransformer + Pipeline
Day 2 — Models & evaluation
- Baselines: linear/logistic, trees, random forest
- Cross-validation and learning curves
- Metrics: RMSE, ROC-AUC, F1, precision/recall
- Error analysis and model debugging
Day 3 — Baseline to production
- Hyperparameter search (Grid/Random)
- Saving/loading models, versioning patterns
- Input validation (schema) and inference contract
- Packaging the predictor for FastAPI integration