Predicting CTR with Machine Learning in Python

Data Scientists, Business Analysts, Software Engineers, Product Managers, and Marketers would benefit from improving their understanding of basic models in Python and applying machine learning to better optimize ads.

Yes, by the end of the course you will be able to accurately predict CTR with the techniques learnt. You will be able to engineer features, build machine learning models using those features, and evaluate your models in the context of CTR prediction.

In this course, you will learn how to perform basic DataFrame manipulation, use machine learning models to predict CTR, apply measures of model performance including precision and recall to answer real-world questions, use ensemble methods and hyperparameter tuning to improve performance metrics, and use deep learning techniques such as multi-layer perceptron (MLP) and neural networks to capture the complex relationship between variables.

Yes, upon completion of the course you will receive a certificate from DataCamp.

To participate in this course you need basic Python skills, such as familiarity with data types, indexing, and manipulating data with Pandas.

In this course, the libraries used are Pandas, Scikit-learn, Matplotlib and TensorFlow.

The Introduction to CTR and Basic Techniques chapter provides an introduction to CTR and covers basic DataFrame manipulation, how to use machine learning models to predict CTR, and feature engineering on categorical features.