STEM / applied
Machine learning · Graduate · Data science
Topics
Implementation depth
- Implementing algorithms from pseudocode
- Modern libraries (PyTorch/sklearn/jax survey per course)
- Distributed training awareness
- Hyperparameter search at scale
- Benchmarking against strong baselines
- Writing clear experimental sections
Responsible ML
- Dataset documentation and leakage audits
- Fairness interventions survey
- Security: adversarial examples awareness
- Environmental cost of large models (survey)
- Deployment constraints in regulated settings
- Research paper reading and critique
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.
- Foundations
Statistical learning framework: risk, ERM, and generalization
Coming soon - Core methods
Trees, boosting, and random forests in depth
Coming soon - Evaluation and theory practice
Proper scoring rules and calibration
Coming soon - Implementation depth
Implementing algorithms from pseudocode
Coming soon - Responsible ML
Dataset documentation and leakage audits
Coming soon
Pricing calculator
Choose materials, tutoring, or both — or book a single session as needed. Customize your plan on the subscribe page.
$1,162 · 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.