This course begins with a basic introduction, and examines some of the strengths and weaknesses of traditional linear regression. We’ll also cover some basics of R, as the examples in this course will use the R programming language to analyze data.
The second module then dives into LASSO models. We see how the LASSO model can solve many of the challenges we face with linear regression, and how it can be a very useful tool for fitting linear models. We also look at a real world use case: forecasting sales at 83 different stores.
The third and final module looks at two additional regularized regression models: Ridge and ElasticNet. We then compare these models, both theoretically and by examining their performance on the forecasting problem from module 2.
What am I going to get from this course?
Implement LASSO, Ridge and Elastic Net models so that they can better analyze data. These models will help them capture relationships in their data, avoid overfitting, and provide models which will predict better than traditional linear regression.
Prerequisites and Target Audience
What will students need to know or do before starting this course?
This course is taught with the programming language R. Students not familiar with R should be prepared to spend a bit extra time catching up on some of the basics of R. Additionally, exposure to linear regression (for example, in an introductory statistics course) would be highly useful.
Who should take this course? Who should not?
- If you want to learn how to start with regularized regression models.
- Currently use linear regressions and want to implement better models.
- Are curious about the ideas of machine learning.
- You should not take this course if you have successfully implemented and used LASSO, Ridge and Elastic Net models in the past (unless you didn’t understand what you were doing).