STEM / applied
Applied machine learning · Undergraduate · Data science
Learning objectives from the Applied machine learning syllabus, grouped by unit. Click an objective for study materials.
Learning objectives
Click an objective for study materials.
Supervised learning core
- Problem framing: prediction vs explanation — Problem framing: prediction vs explanation
- Linear models and regularization (ridge/lasso intro) — Linear models and regularization (ridge/lasso intro)
- Trees, random forests, and boosting survey — Trees, random forests, and boosting survey
- Distance-based and naive Bayes survey — Distance-based and naive Bayes survey
- Feature preprocessing: scaling, encoding, imputation — Feature preprocessing: scaling, encoding, imputation
Evaluation and diagnosis
- Classification and regression metrics — Classification and regression metrics
- Class imbalance strategies — Class imbalance strategies
- Learning curves and under/overfitting diagnosis — Learning curves and under/overfitting diagnosis
- Calibration and probability outputs (intro) — Calibration and probability outputs (intro)
- Error analysis by slice / subgroup — Error analysis by slice / subgroup
Workflow and MLOps intro
- Train/validation/test discipline and leakage — Train/validation/test discipline and leakage
- Pipelines and feature stores (survey) — Pipelines and feature stores (survey)
- Model serialization and versioning — Model serialization and versioning
- Monitoring drift concepts (intro) — Monitoring drift concepts (intro)
- Experiment tracking survey — Experiment tracking survey
What each unit includes
Open a unit below for full materials. Typical resources:
- Study guide
- Exam Strategy
- Common Mistakes
- Worksheets
- Word problems
- Mixed Practice
- Multi-Unit Problems
- Review
- Practice test
- Answer key
Study units
Each unit includes a study guide, worksheets, review, practice test, and answer key. One unit is free; subscribe for the full class.
- Supervised learning core
Problem framing: prediction vs explanation
Coming soon - Evaluation and diagnosis
Classification and regression metrics
Coming soon - Workflow and MLOps intro
Train/validation/test discipline and leakage
Coming soon
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$1,162 · Applied machine learning · 18 tutoring hrs
Study guides, worksheets, reviews, practice tests, and answer keys for 1 class. 18 tutoring hours (1 hr / week · semester). Bundle discount applied vs buying separately. Pay in full via Zelle.