You're reading from Python Machine Learning by Example Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

Product type Paperback

Published in Oct 2020

Publisher Packt

ISBN-13 9781800209718

Length 526 pages

Edition 3rd Edition

Languages

Python

Tools

PyTorch

Concepts

Machine Learning

Author (1):

Yuxi (Hayden) Liu

View More author details

Table of Contents (17) Chapters

Preface

1. Getting Started with Machine Learning and Python

2. Building a Movie Recommendation Engine with Naïve Bayes FREE CHAPTER

3. Recognizing Faces with Support Vector Machine

4. Predicting Online Ad Click-Through with Tree-Based Algorithms

5. Predicting Online Ad Click-Through with Logistic Regression

6. Scaling Up Prediction to Terabyte Click Logs

7. Predicting Stock Prices with Regression Algorithms

8. Predicting Stock Prices with Artificial Neural Networks

9. Mining the 20 Newsgroups Dataset with Text Analysis Techniques

10. Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling

11. Machine Learning Best Practices

12. Categorizing Images of Clothing with Convolutional Neural Networks

13. Making Predictions with Sequences Using Recurrent Neural Networks

14. Making Decisions in Complex Environments with Reinforcement Learning

15. Other Books You May Enjoy

16. Index

A brief overview of ad click-through prediction

Online display advertising is a multibillion-dollar industry. It comes in different formats, including banner ads composed of text, images, and flash, and rich media such as audio and video. Advertisers, or their agencies, place ads on a variety of websites, and even mobile apps, across the Internet in order to reach potential customers and deliver an advertising message.

Online display advertising has served as one of the greatest examples of machine learning utilization. Obviously, advertisers and consumers are keenly interested in well-targeted ads. In the last 20 years, the industry has relied heavily on the ability of machine learning models to predict the effectiveness of ad targeting: how likely it is that an audience of a certain age group will be interested in this product, that customers with a certain household income will purchase this product after seeing the ad, that frequent sports site visitors...