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.

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.

This audiobook includes:

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

Knowledge of Python is assumed for the listener.

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.

Table of Contents:

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

PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.