Cleaning up Acrolinx scores

articles/machine-learning/machine-learning-data-science-customize-hadoop-cluster.md

@@ -20,12 +20,12 @@ ms.author: hangzh;bradsev
 # Customize Azure HDInsight Hadoop clusters for the Team Data Science Process
 This article describes how to customize an HDInsight Hadoop cluster by installing 64-bit Anaconda (Python 2.7) on each node when the cluster is provisioned as an HDInsight service. It also shows how to access the headnode to submit custom jobs to the cluster. This customization makes many popular Python modules, that are included in Anaconda, conveniently available for use in user defined functions (UDFs) that are designed to process Hive records in the cluster. For instructions on the procedures used in this scenario, see [How to submit Hive queries](machine-learning-data-science-move-hive-tables.md#submit).
 
-The menu below links to topics that describe how to set up the various data science environments used by the [Team Data Science Process (TDSP)](data-science-process-overview.md).
+The following menu links to topics that describe how to set up the various data science environments used by the [Team Data Science Process (TDSP)](data-science-process-overview.md).
 
 [!INCLUDE [data-science-environment-setup](../../includes/cap-setup-environments.md)]
 
 ## <a name="customize"></a>Customize Azure HDInsight Hadoop Cluster
-To create a customized HDInsight Hadoop cluster, start by logging on to [**Azure Classic portal**](https://manage.windowsazure.com/), click **New** at the left bottom corner, and then select DATA SERVICES -> HDINSIGHT -> **CUSTOM CREATE** to bring up the **Cluster Details** window. 
+To create a customized HDInsight Hadoop cluster, start by logging on to [**Azure classic portal**](https://manage.windowsazure.com/), click **New** at the left bottom corner, and then select DATA SERVICES -> HDINSIGHT -> **CUSTOM CREATE** to bring up the **Cluster Details** window. 
 
 ![Create workspace](./media/machine-learning-data-science-customize-hadoop-cluster/customize-cluster-img1.png)
 
@@ -46,7 +46,7 @@ On configuration page 3, provide a user name and password for the HDInsight Hado
 
 ![Create workspace](./media/machine-learning-data-science-customize-hadoop-cluster/customize-cluster-img4.png)
 
-On configuration page 4, specify the storage account name, the default container of the HDInsight Hadoop cluster. If you select *Create default container* in the **DEFAULT CONTAINER** drop down list, a container with the same name as the cluster will be created. Click the arrow to go to the last configuration page.
+On configuration page 4, specify the storage account name, the default container of the HDInsight Hadoop cluster. If you select *Create default container* in the **DEFAULT CONTAINER** dropdown list, a container with the same name as the cluster will be created. Click the arrow to go to the last configuration page.
 
 ![Create workspace](./media/machine-learning-data-science-customize-hadoop-cluster/customize-cluster-img5.png)
 
@@ -66,7 +66,7 @@ Finally, click the check mark to start the creation of the customized HDInsight
 ## <a name="headnode"></a> Access the Head Node of Hadoop Cluster
 You must enable remote access to the Hadoop cluster in Azure before you can access the head node of the Hadoop cluster through RDP. 
 
-1. Log in to the [**Classic Portal of Azure**](https://manage.windowsazure.com/), select **HDInsight** on the left, select your Hadoop cluster from the list of clusters, click the **CONFIGURATION** tab, and then click the **ENABLE REMOTE** icon at the bottom of the page.
+1. Log in to the [**Azure classic portal**](https://manage.windowsazure.com/), select **HDInsight** on the left, select your Hadoop cluster from the list of clusters, click the **CONFIGURATION** tab, and then click the **ENABLE REMOTE** icon at the bottom of the page.
 
     ![Create workspace](./media/machine-learning-data-science-customize-hadoop-cluster/enable-remote-access-1.png)
 2. In the **Configure Remote Desktop** window, enter the USER NAME and PASSWORD fields, and select the expiration date for remote access. Then click the check mark to enable the remote access to the head node of the Hadoop cluster.
@@ -84,5 +84,5 @@ After remote access is enabled, click **CONNECT** at the bottom of the page to r
 
 The next steps in the advanced analytics process are mapped in the [Team Data Science Process (TDSP)](https://azure.microsoft.com/documentation/learning-paths/cortana-analytics-process/) and may include steps that move data into HDInsight, then process and sample it there in preparation for learning from the data with Azure Machine Learning.
 
-See [How to submit Hive queries](machine-learning-data-science-move-hive-tables.md#submit) for instructions on how to access the Python modules that are included in Anaconda from the head node of the cluster in user defined functions (UDFs) that are used to process Hive records stored in the cluster.
+See [How to submit Hive queries](machine-learning-data-science-move-hive-tables.md#submit) for instructions on how to access the Python modules that are included in Anaconda from the head node of the cluster in user-defined functions (UDFs) that are used to process Hive records stored in the cluster.
 

articles/machine-learning/machine-learning-data-science-setup-virtual-machine.md

@@ -18,14 +18,14 @@ ms.author: xibingao;bradsev
 
 ---
 # Set up an Azure virtual machine as an IPython Notebook server for advanced analytics
-This topic shows how to provision and configure an Azure virtual machine for advanced analytics that can to be used as part of a data science environment. The Windows virtual machine is configured with supporting tools such as such as IPython Notebook, Azure Storage Explorer, AzCopy, as well as other utilities that are useful for advanced analytics projects. Azure Storage Explorer and AzCopy, for example, provide convenient ways to upload data to Azure blob storage from your local machine or to download it to your local machine from blob storage.
+This topic shows how to provision and configure an Azure virtual machine for advanced analytics that can be used as part of a data science environment. The Windows virtual machine is configured with supporting tools such as IPython Notebook, Azure Storage Explorer, AzCopy, as well as other utilities that are useful for advanced analytics projects. Azure Storage Explorer and AzCopy, for example, provide convenient ways to upload data to Azure blob storage from your local machine or to download it to your local machine from blob storage.
 
-## <a name="create-vm"></a>Step 1: Create a general purpose Azure virtual machine
+## <a name="create-vm"></a>Step 1: Create a general-purpose Azure virtual machine
 If you already have an Azure virtual machine and just want to set up an IPython Notebook server on it, you can skip this step and proceed to [Step 2: Add an endpoint for IPython Notebooks to an existing virtual machine](#add-endpoint).
 
 Before starting the process of creating a virtual machine on Azure, you need to determine the size of the machine that is needed to process the data for their project. Smaller machines have less memory and fewer CPU cores than larger machines, but they are also less expensive. For a list of machine types and prices, see the <a href="http://azure.microsoft.com/pricing/details/virtual-machines/" target="_blank">Virtual Machines Pricing </a> page
 
-1. Log in to <a href="https://manage.windowsazure.com" target="_blank">Azure Classic portal</a>, and click **New** in the bottom left corner. A window will pop up. Select **COMPUTE** -> **VIRTUAL MACHINE** -> **FROM GALLERY**.
+1. Log in to <a href="https://manage.windowsazure.com" target="_blank">Azure classic portal</a>, and click **New** in the bottom left corner. A window will pop up. Select **COMPUTE** -> **VIRTUAL MACHINE** -> **FROM GALLERY**.
 
     ![Create workspace][24]
 2. Choose one of the following images:
@@ -39,7 +39,7 @@ Before starting the process of creating a virtual machine on Azure, you need to
 3. Enter a name for the virtual machine you want to create, select the size of the machine (Default: A3) based on the size of the data the machine is going to process and how powerful you want the machine to be (memory size and the number of compute cores), enter a user name and password for the machine. Then, click the arrow pointing right to go to the next configuration page.
 
     ![Create workspace][26]
-4. Select the **REGION/AFFINITY GROUP/VIRTUAL NETWORK** that contains the **STORAGE ACCOUNT** that you are planning to use for this virtual machine, and then select that storage account. Add an endpoint at the bottom in the **ENDPOINTS**  field by entering the name of the endpoint ("IPython" here). You can choose any string as the **NAME** of the end point, and any integer between 0 and 65536 that is **available** as the **PUBLIC PORT**. The **PRIVATE PORT** has to be **9999**. Users should **avoid** using public ports that have already been assigned for internet services. <a href="http://www.chebucto.ns.ca/~rakerman/port-table.html" target="_blank">Ports for Internet Services</a> provides a list of ports that have been assigned and should be avoided.
+4. Select the **REGION/AFFINITY GROUP/VIRTUAL NETWORK** that contains the **STORAGE ACCOUNT** that you are planning to use for this virtual machine, and then select that storage account. Add an endpoint at the bottom in the **ENDPOINTS**  field by entering the name of the endpoint ("IPython" here). You can choose any string as the **NAME** of the end point, and any integer between 0 and 65536 that is **available** as the **PUBLIC PORT**. The **PRIVATE PORT** has to be **9999**. You should **avoid** using public ports that have already been assigned for internet services. <a href="http://www.chebucto.ns.ca/~rakerman/port-table.html" target="_blank">Ports for Internet Services</a> provides a list of ports that have been assigned and should be avoided.
 
     ![Create workspace][27]
 5. Click the check mark to start the virtual machine provisioning process.
@@ -53,7 +53,7 @@ It may take 15-25 minutes to complete the virtual machine provisioning process.
 ## <a name="add-endpoint"></a>Step 2: Add an endpoint for IPython Notebooks to an existing virtual machine
 If you created the virtual machine by following the instructions in Step 1, then the endpoint for IPython Notebook has already been added and this step can be skipped.
 
-If the virtual machine already exists, and you need to add an endpoint for IPython Notebook that you will install in Step 3 below, first log into Azure Classic Portal, select the virtual machine, and add the endpoint for IPython Notebook server. The following figure contains a screen shot of the portal after the endpoint for IPython Notebook has been added to a Windows virtual machine.
+If the virtual machine already exists, and you need to add an endpoint for IPython Notebook that you will install in Step 3 below, first login to Azure classic portal, select the virtual machine, and add the endpoint for IPython Notebook server. The following figure contains a screen shot of the portal after the endpoint for IPython Notebook has been added to a Windows virtual machine.
 
 ![Create workspace][17]
 
@@ -71,7 +71,7 @@ When prompted, enter a password for the IPython Notebook and the password of the
 ## <a name="access"></a>Step 4: Access IPython Notebooks from a web browser
 To access the IPython Notebook server, open a web browser, and input *https://&#60;virtual machine DNS name>:&#60;public port number>* in the URL text box. Here, the *&#60;public port number>* should  be the port number you specified when the IPython Notebook endpoint was added.
 
-The *&#60;virtual machine DNS name>* can be found at the Classic Portal of Azure. After logging in to the Classic Portal, click **VIRTUAL MACHINES**, select the machine you created, and then select **DASHBOARD**, the DNS name will be shown as follows:
+The *&#60;virtual machine DNS name>* can be found at the Azure classic portal. After logging in to the classic portal, click **VIRTUAL MACHINES**, select the machine you created, and then select **DASHBOARD**, the DNS name will be shown as follows:
 
 ![Create workspace][19]
 
@@ -83,34 +83,34 @@ You will encounter a warning stating that *There is a problem with this website'
 **Chrome:**
 ![Create workspace][21]
 
-After you log on to the IPython Notebook, a directory *DataScienceSamples* will show on the browser. This directory contains sample IPython Notebooks that are shared by Microsoft to help users conduct data science tasks. These sample IPython Notebooks are checked out from [**Github repository**](https://github.com/Azure/Azure-MachineLearning-DataScience/tree/master/Misc/DataScienceProcess/iPythonNotebooks) to the virtual machines during the IPython Notebook server set up process. Microsoft maintains and updates this repository frequently. Users may visit the Github repository to get the most recently updated sample IPython Notebooks.
+After you log on to the IPython Notebook, a directory *DataScienceSamples* will show on the browser. This directory contains sample IPython Notebooks that are shared by Microsoft to help users conduct data science tasks. These sample IPython Notebooks are checked out from [**Github repository**](https://github.com/Azure/Azure-MachineLearning-DataScience/tree/master/Misc/DataScienceProcess/iPythonNotebooks) to the virtual machines during the IPython Notebook server setup process. Microsoft maintains and updates this repository frequently. Users may visit the Github repository to get the most recently updated sample IPython Notebooks.
 ![Create workspace][18]
 
 ## <a name="upload"></a>Step 5: Upload an existing IPython Notebook from a local machine to the IPython Notebook server
-IPython Notebooks provide an easy way for users to upload an existing IPython Notebook on their local machines to the IPython Notebook server on the virtual machines. After users log on to the IPython Notebook in a web browser, click into the **directory** that the IPython Notebook will be uploaded to. Then, select an IPython Notebook .ipynb file to upload from the local machine in the **File Explorer**, and drag and drop it to the IPython Notebook directory on the web browser. Click the **Upload** button, to upload the .ipynb file to the IPython Notebook server. Other users can then start using it in from their web browsers.
+IPython Notebooks provide an easy way for users to upload an existing IPython Notebook on their local machines to the IPython Notebook server on the virtual machines. After you log on to the IPython Notebook in a web browser, click into the **directory** that the IPython Notebook will be uploaded to. Then, select an IPython Notebook .ipynb file to upload from the local machine in the **File Explorer**, and drag and drop it to the IPython Notebook directory on the web browser. Click the **Upload** button, to upload the .ipynb file to the IPython Notebook server. Other users can then start using it in from their web browsers.
 
 ![Create workspace][22]
 
 ![Create workspace][23]
 
-## <a name="shutdown"></a>Shutdown and de-allocate virtual machine when not in use
+## <a name="shutdown"></a>Shut down and de-allocate virtual machine when not in use
 Azure Virtual Machines are priced as **pay only for what you use**. To ensure that you are not being billed when not using your virtual machine, it has to be in the **Stopped (Deallocated)** state when not in use.
 
 > [!NOTE]
-> If you shut down the virtual machine from inside the VM (using Windows power options), the VM is stopped but remains allocated. To ensure you do not continue to be billed, always stop virtual machines from the [Azure Classic Portal](http://manage.windowsazure.com/). You can also stop the VM through Powershell by calling **ShutdownRoleOperation** with "PostShutdownAction" equal to "StoppedDeallocated".
+> If you shut down the virtual machine from inside the VM (using Windows power options), the VM is stopped but remains allocated. To ensure you do not continue to be billed, always stop virtual machines from the [Azure classic portal](http://manage.windowsazure.com/). You can also stop the VM through Powershell by calling **ShutdownRoleOperation** with "PostShutdownAction" equal to "StoppedDeallocated".
 > 
 > 
 
-To shutdown and deallocate the virtual machine:
+To shut down and deallocate the virtual machine:
 
-1. Log in to the [Azure Classic Portal](http://manage.windowsazure.com/) using your account.  
+1. Log in to the [Azure classic portal](http://manage.windowsazure.com/) using your account.  
 2. Select **VIRTUAL MACHINES** from the left navigation bar.
 3. In the list of virtual machines, click on the name of your virtual machine then go to the **DASHBOARD** page.
 4. At the bottom of the page, click **SHUTDOWN**.
 
 ![VM Shutdown][15]
 
-The virtual machine will be deallocated but not deleted. You may restart your virtual machine at any time from the Azure Classic Portal.
+The virtual machine will be deallocated but not deleted. You may restart your virtual machine at any time from the Azure classic portal.
 
 ## Your Azure VM is ready to use: what's next?
 Your virtual machine is now ready to use in your data science exercises. The virtual machine is also ready for use as an IPython Notebook server for the exploration and processing of data, and other tasks in conjunction with Azure Machine Learning and the Team Data Science Process.