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Have you ever wondered how companies like Facebook and Google are able to serve you surprisingly targeted ads that you occasionally click? Well, behind the scenes, they are running sophisticated machine learning models and using rich user data to predict the click-through rate (CTR) for every user who sees those ads. This course will teach you how to implement basic models in Python so that you can see how to better optimize ads with machine learning. Using real-life ad data you’ll learn how to engineer features, build machine learning models using those features, and evaluate your models in the context of CTR prediction. By the end of this course, you’ll have a strong understanding of how you can apply machine learning to make your ads more effective.
Introduction to CTR and Basic TechniquesFree
Chances are you’re on this page because you clicked a link. In this chapter, you’ll learn why click-through-rates (CTR) are integral to targeted advertising, how to perform basic DataFrame manipulation, and how you can use machine learning models to predict CTR.Introduction to click-through rates50 xpBeginning steps100 xpFeature exploration100 xpFirst evaluation of data50 xpOverview of machine learning models50 xpLogistic regression for breast cancer100 xpLogistic regression for images100 xpA second toy model100 xpCTR prediction using decision trees50 xpModel implementation100 xpA first CTR model100 xpBeyond only accuracy100 xp
Exploratory CTR Data Analysis
This chapter provides the foundations for exploratory data analysis (EDA). Using sample data you’ll use the pandas library to look at columns and data types, explore missing data, and use hashing to perform feature engineering on categorical features. All of which are important when exploring features for more accurate CTR prediction.Exploratory data analysis50 xpA first look100 xpChecking for missing values100 xpDistributions by CTR100 xpFeature engineering50 xpAnalyzing datetime columns100 xpConverting categorical variables100 xpCreating new features100 xpStandardizing features50 xpLog normalization100 xpUnderstanding standardization50 xpStandard scaling100 xp
Model Applications and Improvements
It’s time to dive deeper. Find out how you can use measures of model performance including precision and recall to answer real-world questions, such as evaluating ROI on ad spend. You’ll also learn ways to improve upon those evaluation metrics, such as ensemble methods and hyperparameter tuning.Applications of metric evaluation50 xpFour categories of outcomes100 xpEvaluating four categories100 xpROI on ad spend100 xpModel evaluation50 xpPrecision and recall100 xpBaseline100 xpClassifier comparison100 xpTuning models50 xpRegularization100 xpCross validation100 xpModel selection100 xpEnsembles and hyperparameter tuning50 xpUnderstanding hyperparameter tuning50 xpRandom forests100 xpGrid search100 xp
Profits can be heavily impacted by your campaign’s CTR. In this chapter, you’ll learn how deep learning can be used to reduce that risk. You’ll focus on multi-layer perceptron (MLP) and neural network models, and learn how these can be used to capture the complex relationship between variables to more accurately predict CTR. Lastly, you’ll explore how to apply the basics of hyperparameter tuning and regularization to classification models.Introduction to deep learning50 xpUnderstanding MLPs100 xpBeginning model100 xpMLPs for CTR100 xpHyperparameter tuning in deep learning50 xpHyperparameter tuning in MLPs50 xpVarying hyperparameters100 xpMLP Grid Search100 xpModel evaluation50 xpF-beta score100 xpLow precision and high AUC50 xpPrecision, ROI, and AUC100 xpModel review and comparison50 xpModel comparison warmup100 xpEvaluating precision and ROI100 xpTotal scoring100 xpWrap-up video50 xp
Kevin HuoSee More
Kevin is a data scientist who graduated from the University of Pennsylvania with a focus on Computer Science within Engineering, and Statistics and Finance within Wharton. His interests include machine learning, investing, startups, and solving hard problems. He was previously at Facebook and is currently in the hedge fund space in New York.