remove more html

Author: sdgilley

articles/machine-learning/v1/tutorial-1st-experiment-bring-data.md

@@ -163,7 +163,7 @@ optimizer = optim.SGD(
 ```
 
 
-## <a name="upload"></a> Upload the data to Azure
+## Upload the data to Azure
 
 To run this script in Azure Machine Learning, you need to make your training data available in Azure. Your Azure Machine Learning workspace comes equipped with a _default_ datastore. This is an Azure Blob Storage account where you can store your training data.
 

articles/machine-learning/v1/tutorial-1st-experiment-sdk-train.md

@@ -142,7 +142,7 @@ The training code is taken from [this introductory example](https://pytorch.org/
     :::image type="content" source="../media/tutorial-1st-experiment-sdk-train/directory-structure.png" alt-text="Directory structure shows train.py in src subdirectory":::
 
 
-## <a name="test-local"></a> Test locally
+## Test locally
 
 Select **Save and run script in terminal** to run the *train.py* script directly on the compute instance.
 
@@ -258,7 +258,7 @@ If you see an error `Your total snapshot size exceeds the limit`, the **data** f
 Select the **...** at the end of the folder, then select **Move** to move **data** to the **get-started** folder.  
 
 
-## <a name="log"></a> Log training metrics
+## Log training metrics
 
 Now that you have a model training in Azure Machine Learning, start tracking some performance metrics.