STEM / applied
Machine learning intro · Undergraduate · CS / Programming
Topics
Implementation
- Scikit-learn pipelines: preprocessing, fit, predict
- Feature engineering and handling categorical variables
- Hyperparameter tuning with grid/random search
- Model evaluation metrics: accuracy, F1, ROC-AUC, RMSE
- Visualization of decision boundaries and embeddings
Applied ML
- Working with imbalanced data and leakage pitfalls
- Intro to deep learning frameworks (PyTorch/TensorFlow survey)
- Responsible AI: fairness and interpretability (intro)
- Deploying models as batch or API services (intro)
- Case studies: text, vision, or tabular domains
Pipelines and deployment intro
- scikit-learn style fit/predict workflows
- Cross-validation and hyperparameter search (intro)
- Handling imbalanced classes
- Model serialization and simple inference services
- Monitoring drift after deployment (survey)
- Capstone: end-to-end supervised learning mini-project
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 intro · 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.