Interactive Course

Predicting CTR with Machine Learning in Python

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.

  • 4 hours
  • 15 Videos
  • 57 Exercises
  • 610 Participants
  • 4,700 XP

Loved by learners at thousands of top companies:

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Course Description

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.

  1. 1

    Introduction to CTR and Basic Techniques

    Free

    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.

  2. 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.

  3. 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.

  4. Deep Learning

    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.

  1. 1

    Introduction to CTR and Basic Techniques

    Free

    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.

  2. 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.

  3. 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.

  4. Deep Learning

    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.

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Devon

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Devon Edwards Joseph

Lloyd's Banking Group

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Harvard Business School

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Ronald Bowers

Decision Science Analytics @ USAA

Kevin Huo
Kevin Huo

Data Scientist

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.

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Collaborators
  • Lis Sulmont

    Lis Sulmont

  • Maggie Matsui

    Maggie Matsui

Prerequisites
Datasets
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