Merge pull request #213093 from ssalgadodev/conceptAutoReview

Concept auto review

Author: BCS2022

articles/machine-learning/how-to-understand-automated-ml.md

@@ -44,14 +44,12 @@ After your automated ML experiment completes, a history of the jobs can be found
 The following steps and video, show you how to view the run history and model evaluation metrics and charts in the studio:
 
 1. [Sign into the studio](https://ml.azure.com/) and navigate to your workspace.
-1. In the left menu, select **Experiments**.
+1. In the left menu, select **Runs**.
 1. Select your experiment from the list of experiments.
 1. In the table at the bottom of the page, select an automated ML job.
 1. In the **Models** tab, select the **Algorithm name** for the model you want to evaluate.
 1. In the **Metrics** tab, use the checkboxes on the left to view metrics and charts.
 
-![Steps to view metrics in studio](./media/how-to-understand-automated-ml/how-to-studio-metrics.gif)
-
 ## Classification metrics
 
 Automated ML calculates performance metrics for each classification model generated for your experiment. These metrics are based on the scikit learn implementation. 
@@ -204,7 +202,7 @@ spearman_correlation| Spearman correlation is a nonparametric measure of the mon
 
 ### Metric normalization
 
-Automated ML normalizes regression and forecasting metrics which enables comparison between models trained on data with different ranges. A model trained on a data with a larger range has higher error than the same model trained on data with a smaller range, unless that error is normalized.
+Automated ML normalizes regression and forecasting metrics which enable comparison between models trained on data with different ranges. A model trained on a data with a larger range has higher error than the same model trained on data with a smaller range, unless that error is normalized.
 
 While there is no standard method of normalizing error metrics, automated ML takes the common approach of dividing the error by the range of the data: `normalized_error = error / (y_max - y_min)`
 

articles/machine-learning/toc.yml

@@ -675,9 +675,6 @@
   items: 
     - name: Troubleshoot secure workspace connectivity
       href: how-to-troubleshoot-secure-connection-workspace.md
-    - name: VS Code interactive debugging
-      displayName: vscode,remote,debug,pipelines,deployments,ssh
-      href: how-to-debug-visual-studio-code.md
     - name: Troubleshoot SerializationError
       href: how-to-troubleshoot-serialization-error.md
     - name: Troubleshoot descriptor error

articles/machine-learning/v1/toc.yml

@@ -378,6 +378,11 @@
         - name: Troubleshoot the ParallelRunStep
           displayName: debug_batch consume pipeline parallelrunstep inference
           href: how-to-debug-parallel-run-step.md
+    - name: Visual Studio Code
+      items:
+        - name: VS Code interactive debugging
+          displayName: vscode,remote,debug,pipelines,deployments,ssh
+          href: ../how-to-debug-visual-studio-code.md
 - name: Reference (v1)
   items: 
   - name: Machine learning CLI pipeline YAML reference