25949117

Recommendations

Info

Visit us today atmicrosoftpressstore.com• Hundreds of titles available – Books, eBooks, and onlineresources from industry experts• Free U.S. shipping• eBooks in multiple formats – Read on your computer,tablet, mobile device, or e-reader• Print & eBook Best Value Packs• eBook Deal of the Week – Save up to 60% on featured titles• Newsletter and special offers – Be the first tohear about new releases, specials, and more• Register your book – Get additional benefits
Hear aboutit first.Get the latest news from Microsoft Press sent toyour inbox.• New and upcoming books• Special offers• Free eBooks• How-to articlesSign up today at MicrosoftPressStore.com/Newsletters
Page 1: Azure MachineLearningMicrosoft Azur
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 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
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:
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
Page 101 and 102:
FIGURE 4-2 Select the specific impl
Page 103 and 104:
FIGURE 4-5 Sample code for invoking
Page 105 and 106:
FIGURE 4-7 Invoking the NuGet Packa
Page 107 and 108:
FIGURE 4-9 Sample C# code to invoke
Page 109 and 110:
Now watch what happens if we replac
Page 111 and 112:
To do this, simply click the menu o
Page 113 and 114:
FIGURE 4-16 Invoking the Azure Mach
Page 115 and 116:
They fall short, however, in terms
Page 117 and 118:
FIGURE 4-19 Selecting an Empty ASP.
Page 119 and 120:
FIGURE 4-21 Selecting a Web Form it
Page 121 and 122:
Validating the user inputTo further
Page 123 and 124:
FIGURE 4-26 Invoking the Azure Mach
Page 125 and 126:
Create a web service using ASP.NET
Page 127 and 128:
FIGURE 4-29 Selecting the Visual St
Page 129 and 130:
FIGURE 4-31 Adding a new item to th
Page 131 and 132:
FIGURE 4-33 C# class data structure
Page 133 and 134:
FIGURE 4-35 Adding the empty scaffo
Page 135 and 136:
FIGURE 4-38 Installing the NuGet pa
Page 137 and 138:
FIGURE 4-41 The IncomePredictionCon
Page 139 and 140:
{Inputs = new Dictionary<string, St
Page 141 and 142:
}incomePredResult.Sex = mlResultsAr
Page 143 and 144:
FIGURE 4-44 A jQuery Mobile web app
Page 145 and 146:
var iSex = document.getElementById(
Page 147 and 148:
Chapter 5Regression analyticsIn the
Page 149 and 150:
claims. It is easily seen how these
Page 151 and 152:
Note that in this case, the Azure M
Page 153 and 154:
FIGURE 5-4 The Upload A New Dataset
Page 155 and 156:
FIGURE 5-7 Azure ML Studio: Selecti
Page 157 and 158:
FIGURE 5-9 Selecting the option to
Page 159 and 160:
FIGURE 5-12 The Clean Missing Data
Page 161 and 162:
to .80. In this context, .80 means
Page 163 and 164:
FIGURE 5-16 The automobile price pr
Page 165 and 166:
FIGURE 5-18 The option to Visualize
Page 167 and 168:
After clicking Publish Web Service
Page 169 and 170:
FIGURE 5-23 The Azure ML Studio Reg
Page 171 and 172:
SummaryIn this chapter, we took a d
Page 173 and 174:
unsupervised machine learning, the
Page 175 and 176:
Cluster analysis is not necessarily
Page 177 and 178:
Milk Annual spending on milk produc
Page 179 and 180:
Now that we have the Reader module
Page 181 and 182:
After placing the Train Clustering
Page 183 and 184:
he Metric parameter offers two opti
Page 185 and 186:
the results of our K-means clusteri
Page 187 and 188:
Train Clustering Model module remai
Page 189 and 190:
Removes the Train Clustering Model,
Page 191 and 192:
FIGURE 6-24 The wholesale customers
Page 193 and 194:
FIGURE 6-26 The API help sample cod
Page 195 and 196:
FIGURE 6-28 The CSV output file ope
Page 197 and 198:
Chapter 7The Azure ML Matchboxrecom
Page 199 and 200:
Back in the good old days of video
Page 201 and 202:
webpages, recipes, or even other pe
Page 203 and 204:
FIGURE 7-1 The restaurant ratings s
Page 205 and 206:
FIGURE 7-2 Populating the three res
Page 207 and 208:
Now it’s time to add the Train Ma
Page 209 and 210:
Armed with this knowledge, let’s
Page 211 and 212:
The last module to add to our Match
Page 213 and 214:
prediction model will return a set
Page 215 and 216:
FIGURE 7-17 The completed experimen
Page 217 and 218:
Page 219 and 220:
Chapter 8Retraining Azure ML models
Page 221 and 222:
Figure 8-2 illustrates the two appr
Page 223 and 224:
Once you have re-created or located
Page 225 and 226:
as a web service. After processing,
Page 227 and 228:
After adding the three additional W
Page 229 and 230:
FIGURE 8-9 Azure Machine Learning W
Page 231 and 232:
FIGURE 8-11 A sample Azure Machine
Page 233 and 234:
FIGURE 8-13 The two endpoints for t
Page 235 and 236:
Page 237 and 238:
ResourcesFor more information about
Page 239 and 240:
Free ebooksFrom technical overviews
show all

25949117

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?