Microsoft Azure Essentials Azure Machine Learning

Microsoft Azure Essentials Azure Machine Learning

by Jeff Barnes

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Overview

Microsoft Azure Essentials from Microsoft Press is a series of free ebooks designed to help you advance your technical skills with Microsoft Azure.
 
This third ebook in the series introduces Microsoft Azure Machine Learning, a service that a developer can use to build predictive analytics models (using training datasets from a variety of data sources) and then easily deploy those models for consumption as cloud web services. The ebook presents an overview of modern data science theory and principles, the associated workflow, and then covers some of the more common machine learning algorithms in use today. It builds a variety of predictive analytics models using real world data, evaluates several different machine learning algorithms and modeling strategies, and then deploys the finished models as machine learning web services on Azure within a matter of minutes. The ebook also expands on a working Azure Machine Learning predictive model example to explore the types of client and server applications you can create to consume Azure Machine Learning web services.
 
Watch Microsoft Press’s blog and Twitter (@MicrosoftPress) to learn about other free ebooks in the Microsoft Azure Essentials series.

Product Details

ISBN-13: 9780735698185
Publisher: Pearson Education
Publication date: 04/25/2015
Sold by: Barnes & Noble
Format: NOOK Book
Pages: 236
Sales rank: 112,734
File size: 14 MB
Note: This product may take a few minutes to download.

About the Author

Jeff A. Barnes is a Cloud Solution Architect (CSA) on the Microsoft Partner Enterprise Architecture Team, where he engages with leading cloud architects and developers to present Microsoft’s cloud vision. A 17-year Microsoft veteran, Jeff brings over 30 years of deep technical experience to the CSA role. He typically works with key ISVs and global partners to demonstrate how Microsoft Azure technologies can be best leveraged to meet the current and future demands of an organization transitioning to the cloud. Jeff has deep practical experience in the retail, financial, and manufacturing industries and he is a frequent speaker at Microsoft and third-party events. Jeff resides with his family in Miami, Florida, where his definition of “offshore development” usually equates to “fishing offshore.”

Table of Contents

Foreword      6
Introduction     7

Who should read this book     7
Assumptions     8
This book might not be for you if…     8
Organization of this book     8
Conventions and features in this book     9
System requirements     9
Acknowledgments     10
Errata, updates, & support     10
Free ebooks from Microsoft Press     11
Free training from Microsoft Virtual Academy     11
We want to hear from you     11
Stay in touch     12
Chapter 1: Introduction to the science of data     13
What is machine learning?      13
Today’s perfect storm for machine learning     16
Predictive analytics     17
Endless amounts of machine learning fuel     17
Everyday examples of predictive analytics     19
Early history of machine learning     19
Science fiction becomes reality     22
Summary     23
Resources     23
Chapter 2: Getting started with Azure Machine Learning     25
Core concepts of Azure Machine Learning     25
High-level workflow of Azure Machine Learning     26
Azure Machine Learning algorithms     27
Supervised learning     28
Unsupervised learning     33
Deploying a prediction model     34
Show me the money     35
The what, the how, and the why     36
Summary     36
Resources      37
Chapter 3: Using Azure ML Studio      38
Azure Machine Learning terminology     38
Getting started     40
Azure Machine Learning pricing and availability     42
Create your first Azure Machine Learning workspace     44
Create your first Azure Machine Learning experiment     48
Download dataset from a public repository     49
Upload data into an Azure Machine Learning experiment     51
Create a new Azure Machine Learning experiment     53
Visualizing the dataset     55
Split up the dataset     60
Train the model     61
Selecting the column to predict     62
Score the model     65
Visualize the model results     66
Evaluate the model     69
Save the experiment     71
Preparing the trained model for publishing as a web service     71
Create scoring experiment     75
Expose the model as a web service     77
Azure Machine Learning web service BATCH execution     87
Testing the Azure Machine Learning web service     89
Publish to Azure Data Marketplace     91
Overview of the publishing process     92
Guidelines for publishing to Azure Data Marketplace     92
Summary     93
Chapter 4: Creating Azure Machine Learning client and server applications     94
Why create Azure Machine Learning client applications?      94
Azure Machine Learning web services sample code     96
C# console app sample code     99
R sample code     105
Moving beyond simple clients     110
Cross-Origin Resource Sharing and Azure Machine Learning web services     111
Create an ASP.NET Azure Machine Learning web client     111
Making it easier to test our Azure Machine Learning web service     115
Validating the user input     117
Create a web service using ASP.NET Web API     121
Enabling CORS support     130
Processing logic for the Web API web service     133
Summary     142
Chapter 5: Regression analytics     143
Linear regression     143
Azure Machine Learning linear regression example     145
Download sample automobile dataset     147
Upload sample automobile dataset     147
Create automobile price prediction experiment     150
Summary     167
Resources     167
Chapter 6: Cluster analytics     168
Unsupervised machine learning     168
Cluster analysis     169
KNN: K nearest neighbor algorithm     170
Clustering modules in Azure ML Studio     171
Clustering sample: Grouping wholesale customers     172
Operationalizing a K-means clustering experiment     181
Summary     192
Resources     192
Chapter 7: The Azure ML Matchbox recommender     193
Recommendation engines in use today     193
Mechanics of recommendation engines     195
Azure Machine Learning Matchbox recommender background     196
Azure Machine Learning Matchbox recommender: Restaurant ratings     198
Building the restaurant ratings recommender     200
Creating a Matchbox recommender web service     210
Summary     214
Resources     214
Chapter 8: Retraining Azure ML models     215
Workflow for retraining Azure Machine Learning models     216
Retraining models in Azure Machine Learning Studio     217
Modify original training experiment     221
Add an additional web endpoint     224
Retrain the model via batch execution service     229
Summary     232
Resources     233

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