Haralambos (Babis) Marmanis 博士是一個把機器學習技術應用於工業界的先行者，也是供應管理的世界級專傢。Dmitry Babenko曾經為銀行、保險、供應鏈管理與商務智能公司設計過應用與基礎架構。

本書擁有者可以通過 www.manning.com/AlgorithmsoftheIntelligentWeb在綫獲得作者的信息、樣例代碼與免費的電子版本。

Dr. Haralambos (Babis) Marmanis is a pioneer in the adoption of machine learning techniques for industrial solutions, and also a world expert in supply management. He has about twenty years of experience in developing professional software. Currently, he is the director of R&D and chief architect, for expense management solutions, at Emptoris, Inc. Babis holds a Ph.D. in applied mathematics from Brown University, an M.S. degree in theoretical and applied mechanics from the University of Illinois at Urbana-Champaign, and B.S. and M.S. degrees in civil engineering from the Aristotle University of Thessaloniki in Greece. He was the recipient of the Sigma Xi award for innovative research in 2000, and he is the author of numerous publications in peer-reviewed international scientific journals, conferences, and technical periodicals.

Dmitry Babenko is the lead for the data warehouse infrastructure at Emptoris, Inc. He is a software engineer and architect with 13 years of experience in the IT industry. He has designed and built a wide variety of applications and infrastructure frameworks for banking, insurance, supply-chain management, and business intelligence companies. He received a M.S. degree in computer science from Belarussian State University of Informatics and Radioelectronics.

There's priceless insight trapped in the flood of data users leave behind as they interact with web pages and applications. Those insights can be unlocked by using intelligent algorithms like the ones that have earned Facebook, Google, Twitter, and Microsoft a place among the giants of web data pattern extraction. Improved search, data classification, and other smart pattern matching techniques can give an enormous advantage to understanding and interacting with users. Algorithms of the Intelligent Web, Second Edition has been totally revised and teaches the most important approaches to algorithmic web data analysis, enabling readers to create machine learning applications that crunch, munge, and wrangle data collected from users, web applications, sensors, and website logs. Key machine learning concepts are explained and introduced with many code examples in Python's scikit-learn. The book guides readers through the underlying machinery and intelligent algorithms to capture, store, and structure data streams. Readers will explore recommendation engines from the example of Netflix movie recommendations and dive into classification via statistical algorithms, neural networks, and deep learning. They will also consider the ins and outs of ranking and how to test applications based on intelligent algorithms.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.