Advanced machine learning: model pipelines
Once you start fitting models, methodology matters. It is easy simply to pile up complexity, without managing it. Fortunately, we now have best practices (and libraries) that make it easy to iterate over preprocessing, model families, and parameters.
Prerequisites: basic to intermediate knowledge of machine learning (that is, you have run models and optimized parameters, but are sure there must be a better way to do it).
In this course, you will learn:
- How to weight, transform, combine, or drop features
- How to represent transformations, models, parameters, and the results of a run, so they can be easily managed
- What feature transformations add the most performance, and how they interact with the rest of the pipeline