Standard syllabus
Applied machine learning · Undergraduate · Data science
Topics
Supervised learning core
- Problem framing: prediction vs explanation
- Linear models and regularization (ridge/lasso intro)
- Trees, random forests, and boosting survey
- Distance-based and naive Bayes survey
- Feature preprocessing: scaling, encoding, imputation
- Cross-validation and hyperparameter search (intro)
Evaluation and diagnosis
- Classification and regression metrics
- Class imbalance strategies
- Learning curves and under/overfitting diagnosis
- Calibration and probability outputs (intro)
- Error analysis by slice / subgroup
- Baseline models and sanity checks
Workflow and MLOps intro
- Train/validation/test discipline and leakage
- Pipelines and feature stores (survey)
- Model serialization and versioning
- Monitoring drift concepts (intro)
- Experiment tracking survey
- Documentation: model cards (intro)
Pricing calculator
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$60.00 · 60 min · Undergraduate · Online ($60/hr)
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