Final batch

articles/machine-learning/machine-learning-walkthrough-1-create-ml-workspace.md

@@ -24,8 +24,8 @@ This is the first step of the walkthrough, [Develop a predictive analytics solut
 2. [Upload existing data](machine-learning-walkthrough-2-upload-data.md)
 3. [Create a new experiment](machine-learning-walkthrough-3-create-new-experiment.md)
 4. [Train and evaluate the models](machine-learning-walkthrough-4-train-and-evaluate-models.md)
-5. [Deploy the web service](machine-learning-walkthrough-5-publish-web-service.md)
-6. [Access the web service](machine-learning-walkthrough-6-access-web-service.md)
+5. [Deploy the Web service](machine-learning-walkthrough-5-publish-web-service.md)
+6. [Access the Web service](machine-learning-walkthrough-6-access-web-service.md)
 
 - - -
 <!-- This needs to be updated to refer to the new way of creating workspaces in the Ibiza portal -->

articles/machine-learning/machine-learning-walkthrough-2-upload-data.md

@@ -24,8 +24,8 @@ This is the second step of the walkthrough, [Develop a predictive analytics solu
 2. **Upload existing data**
 3. [Create a new experiment](machine-learning-walkthrough-3-create-new-experiment.md)
 4. [Train and evaluate the models](machine-learning-walkthrough-4-train-and-evaluate-models.md)
-5. [Deploy the web service](machine-learning-walkthrough-5-publish-web-service.md)
-6. [Access the web service](machine-learning-walkthrough-6-access-web-service.md)
+5. [Deploy the Web service](machine-learning-walkthrough-5-publish-web-service.md)
+6. [Access the Web service](machine-learning-walkthrough-6-access-web-service.md)
 
 - - -
 To develop a predictive model for credit risk, we need data that we can use to train and then test the model. For this walkthrough, we'll use the "UCI Statlog (German Credit Data) Data Set" from the UC Irvine Machine Learning repository. You can find it here:  

articles/machine-learning/machine-learning-walkthrough-3-create-new-experiment.md

@@ -24,8 +24,8 @@ This is the third step of the walkthrough, [Develop a predictive analytics solut
 2. [Upload existing data](machine-learning-walkthrough-2-upload-data.md)
 3. **Create a new experiment**
 4. [Train and evaluate the models](machine-learning-walkthrough-4-train-and-evaluate-models.md)
-5. [Deploy the web service](machine-learning-walkthrough-5-publish-web-service.md)
-6. [Access the web service](machine-learning-walkthrough-6-access-web-service.md)
+5. [Deploy the Web service](machine-learning-walkthrough-5-publish-web-service.md)
+6. [Access the Web service](machine-learning-walkthrough-6-access-web-service.md)
 
 - - -
 The next step in this walkthrough is to create an experiment in Machine Learning Studio that uses the dataset we uploaded.  

articles/machine-learning/machine-learning-walkthrough-4-train-and-evaluate-models.md

@@ -24,8 +24,8 @@ This topic contains the fourth step of the walkthrough, [Develop a predictive an
 2. [Upload existing data](machine-learning-walkthrough-2-upload-data.md)
 3. [Create a new experiment](machine-learning-walkthrough-3-create-new-experiment.md)
 4. **Train and evaluate the models**
-5. [Deploy the web service](machine-learning-walkthrough-5-publish-web-service.md)
-6. [Access the web service](machine-learning-walkthrough-6-access-web-service.md)
+5. [Deploy the Web service](machine-learning-walkthrough-5-publish-web-service.md)
+6. [Access the Web service](machine-learning-walkthrough-6-access-web-service.md)
 
 - - -
 One of the benefits of using Azure Machine Learning Studio for creating machine learning models is the ability to try more than one type of model at a time in a single experiment and compare the results. This type of experimentation helps you find the best solution for your problem.