grammar + next steps

Author: nibaccam

articles/machine-learning/tutorial-automated-ml-forecast.md

@@ -30,7 +30,7 @@ In this tutorial, you learn how to do the following tasks:
 ## Prerequisites
 
 * An Enterprise edition Azure Machine Learning workspace. If you don't have a workspace, [create an Enterprise edition workspace](how-to-manage-workspace.md). 
-    * Automated machine learning in the Azure Machine Learning studio is only avaialble for Enterprise edition workspaces. 
+    * Automated machine learning in the Azure Machine Learning studio is only available for Enterprise edition workspaces. 
 * Download the [bike-no.csv](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/bike-no.csv) data file
 
 ## Get started in Azure Machine Learning studio
@@ -79,7 +79,7 @@ Before you configure your experiment, upload your data file to your workspace in
 
     1. The **Schema** form allows for further configuration of your data for this experiment. 
 
-        1. For this example, choose to ignore the **casual** and **registered** columns. These columns are a breakdown of the  **cnt** column so, therefore unneccessary.
+        1. For this example, choose to ignore the **casual** and **registered** columns. These columns are a breakdown of the  **cnt** column so, therefore unnecessary.
 
         1. Also for this example, leave the default for
 
@@ -119,7 +119,7 @@ After you load and configure your data, set up your remote compute target and se
 
 ## Select task type and settings
 
-Complete the set up for your automated ml experiment by specifying the machine learnign task type and configuration settings.
+Complete the setup for your automated ml experiment by specifying the machine learning task type and configuration settings.
 
 1. On the **Task type and settings** form, select **Forecasting** as the machine learning task type.
 
@@ -162,7 +162,7 @@ The following navigates through the **Model details** and the **Visualizations**
 
 Automated machine learning in Azure Machine Learning studio allows you to deploy the best model as a web service in a few steps. Deployment is the integration of the model so it can predict on new data and identify potential areas of opportunity. 
 
-For this experiment, deployment to a web service means that the bike share company now has an iterative and scalable web solution for forecasting bikeshare rental demand. 
+For this experiment, deployment to a web service means that the bike share company now has an iterative and scalable web solution for forecasting bikes hare rental demand. 
 
 Once the run is complete, navigate back to the **Run Detail** page and select the **Models** tab.
 
@@ -175,7 +175,7 @@ In this experiment context, **StackEnsemble** is considered the best model, base
     Field| Value
     ----|----
     Deployment name| bikeshare-deploy
-    Deployment description| bikeshare demand deployment
+    Deployment description| bike share demand deployment
     Compute type | Select Azure Compute Instance (ACI)
     Enable authentication| Disable. 
     Use custom deployment assets| Disable. Disabling allows for the default driver file (scoring script) and environment file to be autogenerated. 
@@ -211,7 +211,9 @@ Delete just the deployment instance from the Azure Machine Learning studio, if y
 
 ## Next steps
 
-In this automated machine learning tutorial, you used Azure Machine Learning studio to create and deploy a demand forecasting model. See this article for steps on how to create a Power BI supported schema and how to consume your newly deployed web service in Power BI:
+In this tutorial, you automated ML  in the Azure Machine Learning studio to create and deploy a demand forecasting model. 
+
+See this article for steps on how to create a Power BI supported schema and how to consume your newly deployed web service in Power BI:
 
 > [!div class="nextstepaction"]
 > [Consume a web service](how-to-consume-web-service.md#consume-the-service-from-power-bi)