Predictive Analytics with Microsoft Azure Machine Learning

Build and Deploy Actionable Solutions in Minutes

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

Table of contents

  1. Front Matter
    Pages i-xix
  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 21-42
    4. Roger Barga, Valentine Fontama, Wee Hyong Tok
      Pages 43-64
  3. Statistical and Machine Learning Algorithms

    1. Front Matter
      Pages 65-65
    2. Roger Barga, Valentine Fontama, Wee Hyong Tok
      Pages 67-83
  4. Practical Applications

    1. Front Matter
      Pages 85-85
    2. Roger Barga, Valentine Fontama, Wee Hyong Tok
      Pages 87-106
    3. Roger Barga, Valentine Fontama, Wee Hyong Tok
      Pages 107-127
    4. Roger Barga, Valentine Fontama, Wee Hyong Tok
      Pages 129-142
    5. Roger Barga, Valentine Fontama, Wee Hyong Tok
      Pages 143-161
  5. Back Matter
    Pages 163-166

About this book

Introduction

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.

Bibliographic information

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