fixing links

Author: Larry Franks

articles/machine-learning/v1/how-to-attach-compute-targets.md

@@ -65,7 +65,7 @@ When you use your local computer for **inference**, you must have Docker install
 
 ## Remote virtual machines
 
-Azure Machine Learning also supports attaching an Azure Virtual Machine. The VM must be an Azure Data Science Virtual Machine (DSVM). The VM offers a curated choice of tools and frameworks for full-lifecycle machine learning development. For more information on how to use the DSVM with Azure Machine Learning, see [Configure a development environment](how-to-configure-environment-v1.md#dsvm).
+Azure Machine Learning also supports attaching an Azure Virtual Machine. The VM must be an Azure Data Science Virtual Machine (DSVM). The VM offers a curated choice of tools and frameworks for full-lifecycle machine learning development. For more information on how to use the DSVM with Azure Machine Learning, see [Configure a development environment](how-to-configure-environment-v1.md).
 
 > [!TIP]
 > Instead of a remote VM, we recommend using the [Azure Machine Learning compute instance](../concept-compute-instance.md). It is a fully managed, cloud-based compute solution that is specific to Azure Machine Learning. For more information, see [create and manage Azure Machine Learning compute instance](../how-to-create-manage-compute-instance.md).

articles/machine-learning/v1/how-to-auto-train-models-v1.md

@@ -47,7 +47,7 @@ If you don’t have an Azure subscription, create a free account before you begi
     1. Open the *tutorials/regression-automl-nyc-taxi-data/regression-automated-ml.ipynb* notebook.
     1. To run each cell in the tutorial, select **Clone this notebook**
 
-This article is also available on [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/tutorials) if you wish to run it in your own [local environment](how-to-configure-environment-v1.md#local). 
+This article is also available on [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/tutorials) if you wish to run it in your own [local environment](how-to-configure-environment-v1.md). 
 To get the required packages, 
 * [Install the full `automl` client](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/README.md#setup-using-a-local-conda-environment).
 * Run `pip install azureml-opendatasets azureml-widgets` to get the required packages.

articles/machine-learning/v1/how-to-configure-environment-v1.md

@@ -21,9 +21,9 @@ The following table shows each development environment covered in this article,
 
 | Environment | Pros | Cons |
 | --- | --- | --- |
-| [Local environment](#local) | Full control of your development environment and dependencies. Run with any build tool, environment, or IDE of your choice. | Takes longer to get started. Necessary SDK packages must be installed, and an environment must also be installed if you don't already have one. |
-| [The Data Science Virtual Machine (DSVM)](#dsvm) | Similar to the cloud-based compute instance (Python and the SDK are pre-installed), but with additional popular data science and machine learning tools pre-installed. Easy to scale and combine with other custom tools and workflows. | A slower getting started experience compared to the cloud-based compute instance. |
-| [Azure Machine Learning compute instance](#compute-instance) | Easiest way to get started. The entire SDK is already installed in your workspace VM, and notebook tutorials are pre-cloned and ready to run. | Lack of control over your development environment and dependencies. Additional cost incurred for Linux VM (VM can be stopped when not in use to avoid charges). See [pricing details](https://azure.microsoft.com/pricing/details/virtual-machines/linux/). |
+| [Local environment](#local-computer-or-remote-vm-environment) | Full control of your development environment and dependencies. Run with any build tool, environment, or IDE of your choice. | Takes longer to get started. Necessary SDK packages must be installed, and an environment must also be installed if you don't already have one. |
+| [The Data Science Virtual Machine (DSVM)](#data-science-virtual-machine) | Similar to the cloud-based compute instance (Python and the SDK are pre-installed), but with additional popular data science and machine learning tools pre-installed. Easy to scale and combine with other custom tools and workflows. | A slower getting started experience compared to the cloud-based compute instance. |
+| [Azure Machine Learning compute instance](#azure-machine-learning-compute-instance) | Easiest way to get started. The entire SDK is already installed in your workspace VM, and notebook tutorials are pre-cloned and ready to run. | Lack of control over your development environment and dependencies. Additional cost incurred for Linux VM (VM can be stopped when not in use to avoid charges). See [pricing details](https://azure.microsoft.com/pricing/details/virtual-machines/linux/). |
 | [Azure Databricks](../how-to-configure-databricks-automl-environment.md) | Ideal for running large-scale intensive machine learning workflows on the scalable Apache Spark platform. | Overkill for experimental machine learning, or smaller-scale experiments and workflows. Additional cost incurred for Azure Databricks. See [pricing details](https://azure.microsoft.com/pricing/details/databricks/). |
 
 This article also provides additional usage tips for the following tools:
@@ -36,7 +36,7 @@ This article also provides additional usage tips for the following tools:
 
 * Azure Machine Learning workspace. If you don't have one, you can create an Azure Machine Learning workspace through the [Azure portal](../how-to-manage-workspace.md), [Azure CLI](../how-to-manage-workspace-cli.md#create-a-workspace), and [Azure Resource Manager templates](../how-to-create-workspace-template.md).
 
-### <a id="workspace"></a> Local and DSVM only: Create a workspace configuration file
+### Local and DSVM only: Create a workspace configuration file
 
 The workspace configuration file is a JSON file that tells the SDK how to communicate with your Azure Machine Learning workspace. The file is named *config.json*, and it has the following format:
 
@@ -209,7 +209,7 @@ To use the Data Science VM as a development environment:
 
 1. To configure the Data Science VM to use your Azure Machine Learning workspace, [create a workspace configuration file](#workspace) or use an existing one.
 
-Similar to local environments, you can use Visual Studio Code and the [Azure Machine Learning Visual Studio Code extension](#vscode) to interact with Azure Machine Learning.
+Similar to local environments, you can use Visual Studio Code and the [Azure Machine Learning Visual Studio Code extension](#visual-studio-code) to interact with Azure Machine Learning.
 
 For more information, see [Data Science Virtual Machines](https://azure.microsoft.com/services/virtual-machines/data-science-virtual-machines/).
 

articles/machine-learning/v1/samples-notebooks-v1.md

@@ -45,7 +45,7 @@ These instructions install the base SDK packages necessary for the quickstart an
 
 ## Option 3: Access on a DSVM
 
-The Data Science Virtual Machine (DSVM) is a customized VM image built specifically for doing data science. If you [create a DSVM](how-to-configure-environment-v1.md#dsvm), the SDK and notebook server are installed and configured for you. However, you'll still need to create a workspace and clone the sample repository.
+The Data Science Virtual Machine (DSVM) is a customized VM image built specifically for doing data science. If you [create a DSVM](how-to-configure-environment-v1.md), the SDK and notebook server are installed and configured for you. However, you'll still need to create a workspace and clone the sample repository.
 
 [!INCLUDE [aml-dsvm-server](../../../includes/aml-dsvm-server.md)]
 

articles/machine-learning/v1/tutorial-auto-train-image-models-v1.md

@@ -49,7 +49,7 @@ You'll write code using the Python SDK in this tutorial and learn the following
 
 * Download and unzip the [**odFridgeObjects.zip*](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip) data file. The dataset is annotated in Pascal VOC format, where each image corresponds to an xml file. Each xml file contains information on where its corresponding image file is located and also contains information about the bounding boxes and the object labels. In order to use this data, you first need to convert it to the required JSONL format as seen in the [Convert the downloaded data to JSONL](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/image-object-detection/auto-ml-image-object-detection.ipynb) section of the notebook. 
 
-This tutorial is also available in the [azureml-examples repository on GitHub](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/image-object-detection) if you wish to run it in your own [local environment](how-to-configure-environment-v1.md#local). To get the required packages,
+This tutorial is also available in the [azureml-examples repository on GitHub](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/image-object-detection) if you wish to run it in your own [local environment](how-to-configure-environment-v1.md). To get the required packages,
 * Run `pip install azureml`
 * [Install the full `automl` client](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/README.md#setup-using-a-local-conda-environment)
 

articles/machine-learning/v1/tutorial-train-deploy-notebook.md

@@ -37,7 +37,7 @@ Learn how to take the following actions:
 
 ## Run a notebook from your workspace
 
-Azure Machine Learning includes a cloud notebook server in your workspace for an install-free and pre-configured experience. Use [your own environment](how-to-configure-environment-v1.md#local) if you prefer to have control over your environment, packages, and dependencies.
+Azure Machine Learning includes a cloud notebook server in your workspace for an install-free and pre-configured experience. Use [your own environment](how-to-configure-environment-v1.md) if you prefer to have control over your environment, packages, and dependencies.
 
 ## Clone a notebook folder
 
@@ -85,7 +85,7 @@ You may see a few install warnings.  These can safely be ignored.
 
 ## Run the notebook
 
-This tutorial and accompanying **utils.py** file is also available on [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/tutorials) if you wish to use it on your own [local environment](how-to-configure-environment-v1.md#local). If you aren't using the compute instance, add `%pip install azureml-sdk[notebooks] azureml-opendatasets matplotlib` to the install above.
+This tutorial and accompanying **utils.py** file is also available on [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/tutorials) if you wish to use it on your own [local environment](how-to-configure-environment-v1.md). If you aren't using the compute instance, add `%pip install azureml-sdk[notebooks] azureml-opendatasets matplotlib` to the install above.
 
 > [!Important]
 > The rest of this article contains the same content as you see in the notebook.