fix blocking issues

Author: sdgilley

articles/machine-learning/tutorial-1st-experiment-hello-world.md

@@ -148,7 +148,7 @@ Here's a description of how the control script works:
       `ml_client = MLClient(DefaultAzureCredential(), subscription_id, resource_group, workspace)`
    :::column-end:::
    :::column span="2":::
-      [MLClient](/python/api/azure-ai-ml/azure.ai.ml.mlclient) manages your Azure Machine Learning workspace and it's assets and resources.
+      [MLClient](/python/api/azure-ai-ml/azure.ai.ml.mlclient) manages your Azure Machine Learning workspace and its assets and resources.
    :::column-end:::
 :::row-end:::
 :::row:::

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

@@ -255,7 +255,7 @@ if __name__ == "__main__":
 
 ### View the output
 
-1. In the page that opens, you'll see the job status. The first time you run this script, Azure Machine Learning will build a new Docker image from your PyTorch environment. The whole job might around 10 minutes to complete. This image will be reused in future jobs to make them job much quicker.
+1. In the page that opens, you'll see the job status. The first time you run this script, Azure Machine Learning will build a new Docker image from your PyTorch environment. The whole job might take around 10 minutes to complete. This image will be reused in future jobs to make them job much quicker.
 1. You can see view Docker build logs in the Azure Machine Learning studio. Select the **Outputs + logs** tab, and then select **20_image_build_log.txt**.
 1. When the status of the job is **Completed**, select **Output + logs**.
 1. Select **std_log.txt** to view the output of your job.

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

@@ -204,7 +204,7 @@ To run this script in Azure Machine Learning, you need to make your training dat
     Uploaded 9 files
     ```
 
-## <a name="control-script"></a> Create a control script
+## Create a control script
 
 As you've done previously, create a new Python control script called *run-pytorch-data.py* in the **get-started** folder:
 
@@ -271,7 +271,7 @@ The control script is similar to the one from [part 3 of this series](tutorial-1
    :::column-end:::
 :::row-end:::
 
-## <a name="submit-to-cloud"></a> Submit the run to Azure Machine Learning
+## Submit the run to Azure Machine Learning
 
 Select **Save and run script in terminal**  to run the *run-pytorch-data.py* script.  This run will train the model on the compute cluster using the data you uploaded.
 
@@ -280,7 +280,7 @@ This code will print a URL to the experiment in the Azure Machine Learning studi
 [!INCLUDE [amlinclude-info](../../../includes/machine-learning-py38-ignore.md)]
 
 
-### <a name="inspect-log"></a> Inspect the log file
+### Inspect the log file
 
 In the studio, go to the experiment job (by selecting the previous URL output) followed by **Outputs + logs**. Select the `std_log.txt` file. Scroll down through the log file until you see the following output:
 

articles/machine-learning/v1/tutorial-1st-experiment-hello-world.md

@@ -65,7 +65,7 @@ Your project folder structure will now look like:
 :::image type="content" source="../media/tutorial-1st-experiment-hello-world/directory-structure.png" alt-text="Folder structure shows hello.py in src subfolder.":::
 
 
-### <a name="test"></a>Test your script
+### Test your script
 
 You can run your code locally, which in this case means on the compute instance. Running code locally has the benefit of interactive debugging of code.  
 
@@ -79,7 +79,7 @@ Select **Save and run script in terminal** to run the script.
 
 You'll see the output of the script in the terminal window that opens. Close the tab and select **Terminate** to close the session.
 
-## <a name="control-script"></a> Create a control script
+## Create a control script
 
 A *control script* allows you to run your `hello.py` script on different compute resources. You use the control script to control how and where your machine learning code is run.  
 
@@ -148,7 +148,7 @@ Here's a description of how the control script works:
 :::row-end:::
 
 
-## <a name="submit"></a> Submit and run your code in the cloud
+## Submit and run your code in the cloud
 
 1. Select **Save and run script in terminal** to run your control script, which in turn runs `hello.py` on the compute cluster that you created in the [setup tutorial](../quickstart-create-resources.md).
 
@@ -164,7 +164,7 @@ Here's a description of how the control script works:
 1. When the status of the job is **Completed**, select **Output + logs** at the top of the page.
 1. Select **std_log.txt** to view the output of your job.
 
-## <a name="monitor"></a>Monitor your code in the cloud in the studio
+## Monitor your code in the cloud in the studio
 
 The output from your script will contain a link to the studio that looks something like this:
 `https://ml.azure.com/experiments/hello-world/runs/<run-id>?wsid=/subscriptions/<subscription-id>/resourcegroups/<resource-group>/workspaces/<workspace-name>`.

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

@@ -170,7 +170,7 @@ First you'll create a file with the package dependencies.
     ```
 1. On the toolbar, select **Save** to save the file.  Close the tab if you wish.
 
-## <a name="create-local"></a> Create the control script
+## Create the control script
 
 The difference between the following control script and the one that you used to submit "Hello world!" is that you add a couple of extra lines to set the environment.
 
@@ -225,7 +225,7 @@ if __name__ == "__main__":
    :::column-end:::
 :::row-end:::
 
-## <a name="submit"></a> Submit the run to Azure Machine Learning
+## Submit the run to Azure Machine Learning
 
 1. Select **Save and run script in terminal** to run the *run-pytorch.py* script.
 
@@ -235,7 +235,7 @@ if __name__ == "__main__":
 
 ### View the output
 
-1. In the page that opens, you'll see the job status. The first time you run this script, Azure Machine Learning will build a new Docker image from your PyTorch environment. The whole job might around 10 minutes to complete.  This image will be reused in future jobs to make them run much quicker.
+1. In the page that opens, you'll see the job status. The first time you run this script, Azure Machine Learning will build a new Docker image from your PyTorch environment. The whole job might take around 10 minutes to complete.  This image will be reused in future jobs to make them run much quicker.
 1. You can see view Docker build logs in the Azure Machine Learning studio. Select the **Outputs + logs** tab, and then select **20_image_build_log.txt**.
 1. When the status of the job is **Completed**, select **Output + logs**.
 1. Select **std_log.txt** to view the output of your job.
@@ -367,7 +367,7 @@ dependencies:
 
 Make sure you save this file before you submit the run.
 
-### <a name="submit-again"></a> Submit the run to Azure Machine Learning
+### Submit the run to Azure Machine Learning
 
 Select the tab for the *run-pytorch.py* script, then select **Save and run script in terminal** to re-run the *run-pytorch.py* script.  Make sure you've saved your changes to `pytorch-env.yml` first.