Update tutorial-first-experiment-automated-ml.md

Author: j-martens

articles/machine-learning/service/tutorial-first-experiment-automated-ml.md

@@ -55,10 +55,11 @@ You complete the following experiment set-up and run steps in the workspace land
 
 1. Select **Get started**.
 
-1.  Select **Automated ML** under the **Authoring** section, on the left side pane.
-You'll see the **Getting started** screen, since this is your first experiment with Automated Machine Learning.
+1. In the left pane, select **Automated ML** under the **Authoring** section.
 
-    ![Azure Machine Learning studio](media/tutorial-1st-experiment-automated-ml/get-started.png)
+   Since this is your first automated ML experiment, you'll see the Getting started screen.
+
+   ![Azure Machine Learning studio](media/tutorial-1st-experiment-automated-ml/get-started.png)
 
 1. Select **Create experiment**. 
 
@@ -75,8 +76,7 @@ You'll see the **Getting started** screen, since this is your first experiment w
    >[!NOTE]
    >For this tutorial, you'll use the default storage account and container created with your new compute. They automatically populate in the form.
     
-1. Select **Create** to get the compute target. 
-   **This takes a couple minutes to complete.** 
+1. Select **Create** to get the compute target. <br/> **This takes a couple minutes to complete.** 
 
 1. After creation, select your new compute target from the drop-down list and select **Next**.
 
@@ -89,18 +89,18 @@ You'll see the **Getting started** screen, since this is your first experiment w
     1. Give your dataset a unique name and provide an optional description. 
 
     1. Select **Next** on the bottom left,  to  upload it to the default container that was automatically set up during your workspace creation. Public preview supports only local file uploads. 
-
-    1. When the upload is complete, the **Settings and preview** form is intelligently populated based on the file type. Ensure the form is populated as follows.
+    
+       When the upload is complete, the **Settings and preview** form is intelligently populated based on the file type. 
+       
+    1. Verify that the form is populated as follows and select **Next**.
 
-        Field|Value
+        Field|Value for tutorial
         ---|---
         File format| Delimited
         Delimiter| Comma
         Encoding| UTF-8
         Column headers| All files have same headers
         Skip rows | None
-
-        Select **Next**.
 
     1. The **Schema** form allows for further configuration of your data for this experiment. For this example, select the toggle switch for the **day_of_week** feature, so as to not include it for this experiment. Select **Done**, to complete the file upload and creation of the dataset for your experiment.
 
@@ -111,18 +111,19 @@ You'll see the **Getting started** screen, since this is your first experiment w
 1. Select **y** as the target column, what you want to predict. This column indicates whether the client subscribed to a term deposit or not.
 
 1. Expand **Advanced Settings** and populate the fields as follows.
-
-   >[!NOTE]
-   > For this experiment, you don't set a metric score or max cores per iterations threshold. You also don't block algorithms from being tested.
 
     Advanced&nbsp;settings|Description|Value&nbsp;for&nbsp;tutorial
     ------|---------|---
-    Primary metric| Evaluation metric that the machine learning algorithm will be measured by.|**AUC_weighted** 
-    Exit criteria| When any of these criteria are met, the training job ends even if it didn't fully complete. |Training&nbsp;job&nbsp;time&nbsp;(minutes): **5**  <br> <br> Max&nbsp;#&nbsp;of&nbsp;iterations&#58;**10** 
+    Primary metric| Evaluation metric that the machine learning algorithm will be measured by.|AUC_weighted*
+    Exit criteria| If a criteria is met, the training job is stopped. |Training&nbsp;job&nbsp;time&nbsp;(minutes): 5 <br> <br> Max&nbsp;#&nbsp;of&nbsp;iterations&#58;10
     Preprocessing| Enables preprocessing done by automated machine learning. This includes automatic data cleansing, preparing, and transformation to generate synthetic features.| Enable
-    Validation| Validation type and number of tests. | **K-fold** cross-validation<br><br>  cross-validations: **2** 
-    Concurrency| The number of max concurrent iterations.|**5**
+    Validation type | Choose a cross-validation type.|K-fold cross-validation
+    Number of validations | Number of tests. | 2 cross-validations 
+    Concurrency| The number of max concurrent iterations.|5
 
+   >[!NOTE]
+   > For this experiment, you won't set a metric score or max cores per iterations threshold. Nor will you block algorithms from being tested.
+   
 1. Select **Start** to run the experiment. A screen appears with a status message as the experiment preparation begins.
 
 >[!IMPORTANT]