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 rates

Beginning steps

Feature exploration

First evaluation of data

Overview of machine learning models

Logistic regression for breast cancer

Logistic regression for images

A second toy model

CTR prediction using decision trees

Model implementation steps

Model implementation

A first CTR model

Beyond only accuracy

Introduction to CTR and Basic Techniques

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 analysis

A first look

Checking for missing values

Distributions by CTR

Feature engineering

Analyzing datetime columns

Converting categorical variables

Creating new features

Standardizing features

Log normalization

Understanding standardization

Standard scaling

Exploratory CTR Data Analysis 

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 evaluation

Four categories of outcomes

Evaluating four categories

ROI on ad spend

Model evaluation

Precision and recall

Baseline

Classifier comparison

Tuning models

Regularization

Cross validation

Model selection

Ensembles and hyperparameter tuning

Understanding hyperparameter tuning

Random forests

Grid search

Model Applications and Improvements

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 learning

Understanding MLPs

Beginning model

MLPs for CTR

Hyperparameter tuning in deep learning

Hyperparameter tuning in MLPs

Varying hyperparameters

MLP Grid Search

F-beta score

Low precision and high AUC

Precision, ROI, and AUC

Model review and comparison

Model comparison warmup

Evaluating precision and ROI

Total scoring

Wrap-up video

Deep Learning

Avazu

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.

Data Manipulation with pandas

Learn how to predict click-through rates on ads and implement basic machine learning models in Python so that you can see how to better optimize your ads.

Predicting CTR with Machine Learning in Python

Instructor

Decision trees

Training and testing the model

Evaluation with ROC curve

AUC of ROC curve

Let's practice!

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Introduction to CTR and Basic Techniques - CTR prediction using decision trees

CTR prediction using decision trees

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