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