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Table of ContentsForeword ....................................................................................................................................................................................... 6Introduction ................................................................................................................................................................................. 7Who should read this book ......................................................................................................................................................... 7Assumptions .................................................................................................................................................................................. 8This book might not be for you if… ......................................................................................................................................... 8Organization of this book ............................................................................................................................................................ 8Conventions and features in this book ................................................................................................................................... 9System requirements ...................................................................................................................................................................... 9Acknowledgments .........................................................................................................................................................................10Errata, updates, & support .........................................................................................................................................................10Free ebooks from Microsoft Press...........................................................................................................................................11Free training from Microsoft Virtual Academy ..................................................................................................................11We want to hear from you .........................................................................................................................................................11Stay in touch ....................................................................................................................................................................................12Chapter 1 Introduction to the science of data ............................................................................................................... 13What is machine learning? .........................................................................................................................................................13Today’s perfect storm for machine learning .......................................................................................................................16Predictive analytics ........................................................................................................................................................................17Endless amounts of machine learning fuel..........................................................................................................................17Everyday examples of predictive analytics ..........................................................................................................................19Early history of machine learning ............................................................................................................................................19Science fiction becomes reality ................................................................................................................................................22Summary ...........................................................................................................................................................................................23Resources ..........................................................................................................................................................................................23Chapter 2 Getting started with Azure Machine Learning ........................................................................................... 25Core concepts of Azure Machine Learning .........................................................................................................................25High-level workflow of Azure Machine Learning .............................................................................................................26Azure Machine Learning algorithms ......................................................................................................................................272
Supervised learning ...................................................................................................................................................................... 28Unsupervised learning ................................................................................................................................................................. 33Deploying a prediction model ................................................................................................................................................. 34Show me the money .................................................................................................................................................................... 35The what, the how, and the why ............................................................................................................................................. 36Summary ........................................................................................................................................................................................... 36Resources .......................................................................................................................................................................................... 37Chapter 3 Using Azure ML Studio ...................................................................................................................................... 38Azure Machine Learning terminology .................................................................................................................................. 38Getting started................................................................................................................................................................................ 40Azure Machine Learning pricing and availability ............................................................................................................. 42Create your first Azure Machine Learning workspace .................................................................................................... 44Create your first Azure Machine Learning experiment .................................................................................................. 48Download dataset from a public repository ....................................................................................................................... 49Upload data into an Azure Machine Learning experiment .......................................................................................... 51Create a new Azure Machine Learning experiment ........................................................................................................ 53Visualizing the dataset ................................................................................................................................................................ 55Split up the dataset ....................................................................................................................................................................... 60Train the model .............................................................................................................................................................................. 61Selecting the column to predict .............................................................................................................................................. 62Score the model ............................................................................................................................................................................. 65Visualize the model results ........................................................................................................................................................ 66Evaluate the model ....................................................................................................................................................................... 69Save the experiment ..................................................................................................................................................................... 71Preparing the trained model for publishing as a web service .................................................................................... 71Create scoring experiment ........................................................................................................................................................ 75Expose the model as a web service ........................................................................................................................................ 77Azure Machine Learning web service BATCH execution ............................................................................................... 87Testing the Azure Machine Learning web service ............................................................................................................ 893
Page 1 and 2: Azure MachineLearningMicrosoft Azur
Page 3 and 4: Hear aboutit first.Get the latest n
Page 5: PUBLISHED BYMicrosoft PressA divisi
Page 9 and 10: KNN: K nearest neighbor algorithm .
Page 11 and 12: IntroductionMicrosoft Azure Machine
Page 13 and 14: Chapter 5, “Regression analytics,
Page 15 and 16: If you need additional support, ema
Page 17 and 18: Chapter 1Introduction to the scienc
Page 19 and 20: pack. By combining these deep psych
Page 21 and 22: Predictive analyticsPredictive anal
Page 23 and 24: Everyday examples of predictive ana
Page 25 and 26: 22 million: Average number of mail
Page 27 and 28: it began to learn at an exponential
Page 29 and 30: Chapter 2Getting started with Azure
Page 31 and 32: Data It’s all about the data. Her
Page 33 and 34: The concept of supervised learning
Page 35 and 36: to determine the model’s accuracy
Page 37 and 38: FIGURE 2-6 Testing the new predicti
Page 39 and 40: The web service then passes the inc
Page 41 and 42: Creating new prediction models usin
Page 43 and 44: Data center location Defines the ph
Page 45 and 46: This is a free Azure offer that is
Page 47 and 48: In terms of where you can host an A
Page 49 and 50: An Azure Machine Learning workspace
Page 51 and 52: Machine Learning Workspace features
Page 53 and 54: Machine Learning Studio and then pu
Page 55 and 56: Number of instances in the dataset
Page 57 and 58:
Create a new Azure Machine Learning
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experiment. Figure 3-13 shows the A
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FIGURE 3-16 Visualization of the Ad
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The workclass field is designated a
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Figure 3-20 displays a screenshot o
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FIGURE 3-22 Opening the column sele
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FIGURE 3-24 Training our new Azure
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FIGURE 3-26 Visualizing the results
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Evaluate the modelOne of the most c
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Receiver Operator Characteristic (R
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FIGURE 3-31 After the Prepare Web S
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If we click the toggle button again
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FIGURE 3-37 The Azure Machine Learn
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provide access to data over the Web
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Note that the sample Request Body d
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FIGURE 3-47 A sample Response Body
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Sample Code Here’s the best part:
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Testing the Azure Machine Learning
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FIGURE 3-53 Processing the Azure Ma
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ce-to-azure-marketplace/http://data
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The following is a listing of the a
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FIGURE 4-2 Select the specific impl
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FIGURE 4-5 Sample code for invoking
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FIGURE 4-7 Invoking the NuGet Packa
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FIGURE 4-9 Sample C# code to invoke
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Now watch what happens if we replac
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To do this, simply click the menu o
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FIGURE 4-16 Invoking the Azure Mach
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They fall short, however, in terms
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FIGURE 4-19 Selecting an Empty ASP.
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FIGURE 4-21 Selecting a Web Form it
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Validating the user inputTo further
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FIGURE 4-26 Invoking the Azure Mach
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Create a web service using ASP.NET
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FIGURE 4-29 Selecting the Visual St
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FIGURE 4-31 Adding a new item to th
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FIGURE 4-33 C# class data structure
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FIGURE 4-35 Adding the empty scaffo
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FIGURE 4-38 Installing the NuGet pa
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FIGURE 4-41 The IncomePredictionCon
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{Inputs = new Dictionary<string, St
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}incomePredResult.Sex = mlResultsAr
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FIGURE 4-44 A jQuery Mobile web app
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var iSex = document.getElementById(
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Chapter 5Regression analyticsIn the
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claims. It is easily seen how these
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Note that in this case, the Azure M
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FIGURE 5-4 The Upload A New Dataset
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FIGURE 5-7 Azure ML Studio: Selecti
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FIGURE 5-9 Selecting the option to
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FIGURE 5-12 The Clean Missing Data
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to .80. In this context, .80 means
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FIGURE 5-16 The automobile price pr
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FIGURE 5-18 The option to Visualize
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After clicking Publish Web Service
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FIGURE 5-23 The Azure ML Studio Reg
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SummaryIn this chapter, we took a d
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unsupervised machine learning, the
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Cluster analysis is not necessarily
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Milk Annual spending on milk produc
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Now that we have the Reader module
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After placing the Train Clustering
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he Metric parameter offers two opti
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the results of our K-means clusteri
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Train Clustering Model module remai
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Removes the Train Clustering Model,
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FIGURE 6-24 The wholesale customers
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FIGURE 6-26 The API help sample cod
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FIGURE 6-28 The CSV output file ope
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Chapter 7The Azure ML Matchboxrecom
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Back in the good old days of video
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webpages, recipes, or even other pe
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FIGURE 7-1 The restaurant ratings s
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FIGURE 7-2 Populating the three res
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Now it’s time to add the Train Ma
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Armed with this knowledge, let’s
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The last module to add to our Match
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prediction model will return a set
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FIGURE 7-17 The completed experimen
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Chapter 8Retraining Azure ML models
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Figure 8-2 illustrates the two appr
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Once you have re-created or located
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as a web service. After processing,
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After adding the three additional W
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FIGURE 8-9 Azure Machine Learning W
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FIGURE 8-11 A sample Azure Machine
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FIGURE 8-13 The two endpoints for t
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ResourcesFor more information about
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