By the end of this course you should be able to:
- Differentiate uses and applications of classification and regression in the context of supervised machine learning.
- Describe and use linear regression models.
- Use a variety of error metrics to compare and select a linear regression model that best suits your data.
- Articulate why regularization may help prevent overfitting.
- Use regularization regressions: Ridge, LASSO, and Elastic net.