1. Wrap-up video
Well done on completing this course! Let's quickly review the main points you learned from each chapter.
2. Chapter 1
In chapter one, you first did an introduction to CTRs where you learned about the basic problem from a classification lens. Then you had a quick overview of machine learning models, where you practiced using logistic regression on various datasets. Finally, you applied decision trees for CTR prediction.
3. Chapter 2
In chapter two, you first did an introduction to exploratory data analysis (EDA) where you looked at specific features and variability with CTR. Then you did some feature engineering through hashing and creating features from existing ones. Finally, you applied standard scaling and log normalization on those features.
4. Chapter 3
In chapter three, you first learned about the business interpretations of the four categories of outcomes through confusion matrices and a ROI framework. Then you evaluated the precision and recall of a classifier relative to a baseline one. Next, you learned the concepts of regularization and cross-validation. Finally, you tuned hyperparameters using grid search for an ensemble method, Random Forests.
5. Chapter 4
In chapter four, you first learned about inner workings of neural networks. Then you tuned various hyperparameters, including hidden layer sizes and max iterations. Next, you computed F-beta scores and learned about precision versus AUC of the ROC curve. Finally, you reviewed and compared all models on all evaluation metrics, finding Decision Trees to be the most effective.
6. Thank you!
Now you have all the tools and concepts needed to better predict CTR through machine learning. Before you run an ad campaign, make sure to consider the relevant evaluation metrics that matter to you, and look into predicting CTR to lead to higher ROI outcomes. Congratulations on completing the course!