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
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$60.00 · 60 min · Undergraduate · Online ($60/hr)
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