Description
Practical Machine Learning: Prediction, Trees, and Regularization in R
This **Johns Hopkins course** focuses on the **applied side of ML**: using the caret package in R to build, tune, and evaluate predictive models—without getting lost in theory. You’ll work with real datasets to solve classification and regression problems using best practices.
Algorithms You’ll Use
- Decision trees & random forests
- Regularized regression—ridge, lasso, elastic net
- Support vector machines
- Ensemble methods—bagging, boosting
Key Skills
- Preprocessing—centering, scaling, dummy variables
- Resampling—cross-validation, bootstrapping
- Feature selection—recursive elimination, importance scores
- Model comparison—ROC, AUC, RMSE
Who Should Enroll?
- Analysts moving from descriptive to predictive analytics
- R users wanting to apply ML without switching to Python
- Students in the JHU Data Science Specialization
- Researchers needing interpretable models
ML That Works in Practice
This course skips neural nets and focuses on **algorithms that work on tabular data**—the majority of real-world business problems.
Ready to build predictive models in R? Enroll now.
