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New Storage Account Name If you opt to create a new storage account for your AzureMachine Learning workspace, be sure that the storage account name is only made up oflowercase alphanumeric characters. You will know if it is unique by the presence of the greencheck mark to the right of the text box.Once you click Create An ML Workspace, Azure will then provision a brand new Azure MachineLearning workspace for you to use to create and host your Azure Machine Learning experiments.After your Azure Machine Learning Workspace has been created, click your new Azure MachineLearning Workspace and you should see a screen similar to Figure 3-6.FIGURE 3-6 A new Azure Machine Learning workspace.Note that this is the main landing page for managing an Azure Machine Learning workspace. Fromthis page, you can directly access the Azure Machine Learning Studio tools for your workspace, manageuser access to your workspace, and manage any web services that you might have deployed as a resultof your experiments in your workspace. The top navigation menu provides access to several Azure46
Machine Learning Workspace features.Dashboard Here you can monitor the relative or absolute computational usage of yourworkspace over a period of time.Configure This feature is used to allow or deny user access to your Azure Machine Learningworkspace.Web Services This option allows you to manage web services, configure endpoints, and accessyour API via request/response or batch code samples in C#, Python, or in the R language, apopular programming language for data scientists and statisticians.We revisit these features in detail later as we continue our exploration of the Azure MachineLearning environment.Under Access Your Workspace, click the Sign-In To ML Studio link to sign in to your new AzureMachine Learning workspace. Figure 3-7 is a screenshot of the Azure ML Studio inside a newworkspace.FIGURE 3-7 The Azure ML Studio inside of an Azure Machine Learning workspace.47
Page 1 and 2: Azure MachineLearningMicrosoft Azur
Page 3 and 4: Hear aboutit first.Get the latest n
Page 5 and 6: PUBLISHED BYMicrosoft PressA divisi
Page 7 and 8: Supervised learning ...............
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: An Azure Machine Learning workspace
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
Page 59 and 60: experiment. Figure 3-13 shows the A
Page 61 and 62: FIGURE 3-16 Visualization of the Ad
Page 63 and 64: The workclass field is designated a
Page 65 and 66: Figure 3-20 displays a screenshot o
Page 67 and 68: FIGURE 3-22 Opening the column sele
Page 69 and 70: FIGURE 3-24 Training our new Azure
Page 71 and 72: FIGURE 3-26 Visualizing the results
Page 73 and 74: Evaluate the modelOne of the most c
Page 75 and 76: Receiver Operator Characteristic (R
Page 77 and 78: FIGURE 3-31 After the Prepare Web S
Page 79 and 80: If we click the toggle button again
Page 81 and 82: FIGURE 3-37 The Azure Machine Learn
Page 83 and 84: FIGURE 3-40 The Azure Machine Learn
Page 85 and 86: provide access to data over the Web
Page 87 and 88: Note that the sample Request Body d
Page 89 and 90: FIGURE 3-47 A sample Response Body
Page 91 and 92: Sample Code Here’s the best part:
Page 93 and 94: Testing the Azure Machine Learning
Page 95 and 96: FIGURE 3-53 Processing the Azure Ma
Page 97 and 98: ce-to-azure-marketplace/http://data
Page 99 and 100: 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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FIGURE 7-19 The Azure Machine Learn
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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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FIGURE 8-15 The Azure Machine Learn
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ResourcesFor more information about
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