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Algorithms of the Intelligent Web
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Algorithms of the Intelligent Web in Chattanooga, TN
Current price: $44.99
Barnes and Noble
Algorithms of the Intelligent Web in Chattanooga, TN
Current price: $44.99
Loading Inventory...
Size: Paperback
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
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
Building applications for the intelligent web
Extracting structure from data: clustering and transforming your data
Recommending relevant content
Classification: placing things where they belong
Case study: click prediction for online advertising
Making the right choice
The future of the intelligent web
Appendix - Capturing data on the web
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
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
Building applications for the intelligent web
Extracting structure from data: clustering and transforming your data
Recommending relevant content
Classification: placing things where they belong
Case study: click prediction for online advertising
Making the right choice
The future of the intelligent web
Appendix - Capturing data on the web
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
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
Building applications for the intelligent web
Extracting structure from data: clustering and transforming your data
Recommending relevant content
Classification: placing things where they belong
Case study: click prediction for online advertising
Making the right choice
The future of the intelligent web
Appendix - Capturing data on the web
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
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
Building applications for the intelligent web
Extracting structure from data: clustering and transforming your data
Recommending relevant content
Classification: placing things where they belong
Case study: click prediction for online advertising
Making the right choice
The future of the intelligent web
Appendix - Capturing data on the web
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