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Predictive Analytics with Microsoft Azure Machine Learning

  • Authors
  • Roger Barga
  • Valentine Fontama
  • Wee Hyong Tok

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Introducing Data Science and Microsoft Azure Machine Learning

    1. Front Matter
      Pages 1-1
    2. Roger Barga, Valentine Fontama, Wee Hyong Tok
      Pages 3-20
    3. Roger Barga, Valentine Fontama, Wee Hyong Tok
      Pages 21-43
    4. Roger Barga, Valentine Fontama, Wee Hyong Tok
      Pages 45-79
    5. Roger Barga, Valentine Fontama, Wee Hyong Tok
      Pages 81-101
    6. Roger Barga, Valentine Fontama, Wee Hyong Tok
      Pages 103-130
  3. Statistical and Machine Learning Algorithms

    1. Front Matter
      Pages 131-131
    2. Roger Barga, Valentine Fontama, Wee Hyong Tok
      Pages 133-148
  4. Practical Applications

    1. Front Matter
      Pages 149-149
    2. Roger Barga, Valentine Fontama, Wee Hyong Tok
      Pages 151-171
    3. Roger Barga, Valentine Fontama, Wee Hyong Tok
      Pages 173-188
    4. Roger Barga, Valentine Fontama, Wee Hyong Tok
      Pages 189-206
    5. Roger Barga, Valentine Fontama, Wee Hyong Tok
      Pages 207-220
    6. Roger Barga, Valentine Fontama, Wee Hyong Tok
      Pages 221-241
    7. Roger Barga, Valentine Fontama, Wee Hyong Tok
      Pages 243-262
    8. Roger Barga, Valentine Fontama, Wee Hyong Tok
      Pages 263-277
    9. Roger Barga, Valentine Fontama, Wee Hyong Tok
      Pages 279-283
  5. Back Matter
    Pages 285-291

About this book

Introduction

Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book provides a thorough overview of the Microsoft Azure Machine Learning service released for general availability on February 18th, 2015 with practical guidance for building recommenders, propensity models, and churn and predictive maintenance models.

The authors use task oriented descriptions and concrete end-to-end examples to ensure that the reader can immediately begin using this new service. The book describes all aspects of the service from data ingress to applying machine learning, evaluating the models, and deploying them as web services.

Learn how you can quickly build and deploy sophisticated predictive models with the new Azure Machine Learning from Microsoft.

What’s New in the Second Edition?

Five new chapters have been added with practical detailed coverage of:

  • Python Integration – a new feature announced February 2015
  • Data preparation and feature selection
  • Data visualization with Power BI
  • Recommendation engines
  • Selling your models on Azure Marketplace

Bibliographic information

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