Algorithms of the Intelligent Web, 2nd Edition

Summary

Algorithms of the Intelligent Web, Second Edition teaches the most important approaches to algorithmic web data analysis, enabling you to create your own machine learning applications that crunch, munge, and wrangle data collected from users, web applications, sensors and website logs.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

Valuable insights are buried in the tracks web users leave as they navigate pages and applications. You can uncover them by using intelligent algorithms like the ones that have earned Facebook, Google, and Twitter a place among the giants of web data pattern extraction.

About the Book

Algorithms of the Intelligent Web, Second Edition teaches you how to create machine learning applications that crunch and wrangle data collected from users, web applications, and website logs. In this totally revised edition, you’ll look at intelligent algorithms that extract real value from data. Key machine learning concepts are explained with code examples in Python’s scikit-learn. This book guides you through algorithms to capture, store, and structure data streams coming from the web. You’ll explore recommendation engines and dive into classification via statistical algorithms, neural networks, and deep learning.

What’s Inside

• Introduction to machine learning
• Extracting structure from data
• Deep learning and neural networks
• How recommendation engines work

About the Reader

Knowledge of Python is assumed.

About the Authors

Douglas McIlwraith is a machine learning expert and data science practitioner in the field of online advertising. Dr. Haralambos Marmanis is a pioneer in the adoption of machine learning techniques for industrial solutions. Dmitry Babenko designs applications for banking, insurance, and supply-chain management. Foreword by Yike Guo.

Table of Contents

Chapter 1. Building applications for the intelligent web
Chapter 2. Extracting structure from data: clustering and transforming your data
Chapter 3. Recommending relevant content
Chapter 4. Classification: placing things where they belong
Chapter 5. Case study: click prediction for online advertising
Chapter 6. Deep learning and neural networks
Chapter 7. Making the right choice
Chapter 8. The future of the intelligent web
Appendix Capturing data on the web