During one of the five labs of this course, you will use our dataset to understand the various techniques involved and build a fully working Click-through Rate Prediction Pipeline.
Among other things, you will learn how to:
- extract different types of data (numerical and categorical) using one-hot encoding or feature hashing (for dimensionality reduction);
- code gradient descent, various loss functions and regression algorithms;
- use state of the art libs (MLlib) to train models;
- optimize models via hyper-parameter tuning using grid search;
- interpret the probabilistic classifier via a ROC plot.
It is not allowed to post solution examples of the course, but you will find below some real illustrations from the lab.
More than 47.000 students have already enrolled in this course. Will you be one of them?
- Kaggle Display Advertising Challenge Dataset
Loic Le Bel
Lead Software Developer, R&D