Book Image

Python Machine Learning By Example - Third Edition

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning By Example - Third Edition

By: Yuxi (Hayden) Liu

Overview of this book

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.
Table of Contents (17 chapters)
15
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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...