Algorithms of the Intelligent Web, Second Edition
Douglas G. McIlwraith, Haralambos Marmanis, and Dmitry Babenko
Foreword by Yike Guo
  • August 2016
  • ISBN 9781617292583
  • 240 pages
  • printed in black & white
free previous edition eBook included
An eBook copy of the previous edition of this book is included at no additional cost. It will be automatically added to your Manning Bookshelf within 24 hours of purchase.

Concise descriptions of algorithms with their mathematical foundation and sample code in Python.

From the Foreword by Yike Guo, Data Science Institute, Imperial College London

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.

Listen to this book in liveAudio! liveAudio integrates a professional voice recording with the book’s text, graphics, code, and exercises in Manning’s exclusive liveBook online reader. Use the text to search and navigate the audio, or download the audio-only recording for portable offline listening. You can purchase or upgrade to liveAudio here or in liveBook.

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.


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