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Predictive Analytics with Microsoft Azure Machine Learning

Build and Deploy Actionable Solutions in Minutes

Authors: Fontama, Valentine, Barga, Roger, Tok, Wee Hyong

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  • The first book on the market to provide an overview and specific details of Microsoft's new predictive analytics service, Azure Machine Learning
  • Provides a structured approach to Data Science and practical guidance for solving real world business problems such as buyer propensity modeling, customer churn analysis, predictive maintenance and product recommendation
  • Explains how you can quickly build and deploy sophisticated predictive models as machine learning web services
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Buy this book

eBook $39.99
price for USA
  • ISBN 978-1-4842-0445-0
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Download immediately after purchase
Softcover $49.99
price for USA
  • ISBN 978-1-4842-0446-7
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

Data Science and Machine Learning are in high demand, as customers are increasingly looking for ways to glean insights from all their data. More customers now realize that Business Intelligence is not enough as the volume, speed and complexity of data now defy traditional analytics tools. While Business Intelligence addresses descriptive and diagnostic analysis, Data Science unlocks new opportunities through predictive and prescriptive analysis.

The purpose of this book is to provide a gentle and instructionally organized introduction to the field of data science and machine learning, with a focus on building and deploying predictive models.

The book also provides a thorough overview of the Microsoft Azure Machine Learning service using task oriented descriptions and concrete end-to-end examples, sufficient to ensure the reader can immediately begin using this important new service. It describes all aspects of the service from data ingress to applying machine learning and evaluating the resulting model, to deploying the resulting model as a machine learning web service. Finally, this book attempts to have minimal dependencies, so that you can fairly easily pick and choose chapters to read. When dependencies do exist, they are listed at the start and end of the chapter.

The simplicity of this new service from Microsoft will help to take Data Science and Machine Learning to a much broader audience than existing products in this space. Learn how you can quickly build and deploy sophisticated predictive models as machine learning web services with the new Azure Machine Learning service from Microsoft.

About the authors

Valentine Fontama is a Principal Data Scientist in the Data and Decision Sciences Group (DDSG) at Microsoft, where he leads external consulting engagements that deliver world-class Advanced Analytics solutions to Microsoft’s customers. Val has over 18 years of experience in data science and business. Following a PhD in Artificial Neural Networks, he applied data mining in the environmental science and credit industries. Before Microsoft, Val was a New Technology Consultant at Equifax in London where he pioneered the application of data mining to risk assessment and marketing in the consumer credit industry. He is currently an Affiliate Professor of Data Science at the University of Washington. In his prior role at Microsoft, Val was a Senior Product Marketing Manager responsible for big data and predictive analytics in cloud and enterprise marketing. In this role, he led product management for Microsoft Azure Machine Learning; HDInsight, the first Hadoop service from Microsoft; Parallel Data Warehouse, Microsoft’s first data warehouse appliance; and three releases of Fast Track Data Warehouse. He also played a key role in defining Microsoft’s strategy and positioning for in-memory computing.Val holds an M.B.A. in Strategic Management and Marketing from Wharton Business School, a Ph.D. in Neural Networks, a M.Sc. in Computing, and a B.Sc. in Mathematics and Electronics (with First Class Honors). He co-authored the book Introducing Microsoft Azure HDInsight, and has published 11 academic papers with 152 citations by over 227 authors.

Table of contents (8 chapters)

  • Introduction to Data Science

    Barga, Roger (et al.)

    Pages 3-20

  • Introducing Microsoft Azure Machine Learning

    Barga, Roger (et al.)

    Pages 21-42

  • Integration with R

    Barga, Roger (et al.)

    Pages 43-64

  • Introduction to Statistical and Machine Learning Algorithms

    Barga, Roger (et al.)

    Pages 67-83

  • Building Customer Propensity Models

    Barga, Roger (et al.)

    Pages 87-106

Buy this book

eBook $39.99
price for USA
  • ISBN 978-1-4842-0445-0
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Download immediately after purchase
Softcover $49.99
price for USA
  • ISBN 978-1-4842-0446-7
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.

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Bibliographic Information

Bibliographic Information
Book Title
Predictive Analytics with Microsoft Azure Machine Learning
Book Subtitle
Build and Deploy Actionable Solutions in Minutes
Authors
Copyright
2014
Publisher
Apress
Copyright Holder
Valentine Fontama and Roger Barga and Wee Hyong Tok
eBook ISBN
978-1-4842-0445-0
DOI
10.1007/978-1-4842-0445-0
Softcover ISBN
978-1-4842-0446-7
Edition Number
1
Number of Pages
XVI, 188
Number of Illustrations and Tables
116 b/w illustrations
Topics