Python for Data Science: Session Five

Session Content

Teaching Session Content

Train-test splits and over/under-fitting
Classification with k-nearest neighbours
Regression with support vector machines
Clustering with k-means
Ensemble methods (bagging) with random forests
Cross-validation and hyperparameter tuning

Project Session Content

TBD

Recommended Reading and Resources

Scikit-learn API Reference
Scikit-learn User Guide
Scikit-learn Examples
Google’s ML Crash Course

Session Resources

Teaching Session

Project Session