After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Описание курса
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.
Предварительные требования
Программа
Структура курса
1
Introduction to CTR and Basic Techniques
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 XP
- Beginning steps100 XP
- Feature exploration100 XP
- First evaluation of data50 XP
- Overview of machine learning models50 XP
- Logistic regression for breast cancer100 XP
- Logistic regression for images100 XP
- A second toy model100 XP
- CTR prediction using decision trees50 XP
- Model implementation100 XP
- A first CTR model100 XP
- Beyond only accuracy100 XP
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.
Predicting CTR with Machine Learning in Python
Курс
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