Fix typos.

Author: Marc Gelormino

articles/machine-learning/how-to-auto-train-forecast.md

@@ -124,7 +124,7 @@ The [`AutoMLConfig`](https://docs.microsoft.com/python/api/azureml-train-automl-
 
 See the [reference documentation](/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig) for more information.
 
-Create the time-series settings as a dictionary object. Set the `time_column_name` to the `day_datetime` field in the data set. Define the `grain_column_names` parameter to ensure that **two separate time-series groups** are created for the data; one for store A and B. Lastly, set the `max_horizon` to 50 in order to predict for the entire test set. Set a forecast window to 10 periods with `target_rolling_window_size`, and specify a single lag on the target values for two periods ahead with the `target_lags` parameter. It is recommended to set `max_horizon`, `target_rolling_window_size` and `target_lags` to "auto" which will automatically detect these values for you. In the example below, "auto" settings have been used for these paramaters. 
+Create the time-series settings as a dictionary object. Set the `time_column_name` to the `day_datetime` field in the data set. Define the `grain_column_names` parameter to ensure that **two separate time-series groups** are created for the data; one for store A and B. Lastly, set the `max_horizon` to 50 in order to predict for the entire test set. Set a forecast window to 10 periods with `target_rolling_window_size`, and specify a single lag on the target values for two periods ahead with the `target_lags` parameter. It is recommended to set `max_horizon`, `target_rolling_window_size` and `target_lags` to "auto" which will automatically detect these values for you. In the example below, "auto" settings have been used for these parameters. 
 
 ```python
 time_series_settings = {
@@ -231,7 +231,7 @@ Calculate RMSE (root mean squared error) between the `actual_labels` actual valu
 from sklearn.metrics import mean_squared_error
 from math import sqrt
 
-rmse = sqrt(mean_squared_error(actual_lables, predict_labels))
+rmse = sqrt(mean_squared_error(actual_labels, predict_labels))
 rmse
 ```
 

articles/machine-learning/how-to-secure-web-service.md

@@ -78,7 +78,7 @@ To deploy (or redeploy) the service with SSL enabled, set the *ssl_enabled* para
 
 When you deploy to AKS, you can create a new AKS cluster or attach an existing one. For more information on creating or attaching a cluster, see [Deploy a model to an Azure Kubernetes Service cluster](how-to-deploy-azure-kubernetes-service.md).
 
--  If you create a new cluster, you use **[AksCompute.provisionining_configuration()](/python/api/azureml-core/azureml.core.compute.akscompute?view=azure-ml-py#provisioning-configuration-agent-count-none--vm-size-none--ssl-cname-none--ssl-cert-pem-file-none--ssl-key-pem-file-none--location-none--vnet-resourcegroup-name-none--vnet-name-none--subnet-name-none--service-cidr-none--dns-service-ip-none--docker-bridge-cidr-none--cluster-purpose-none--load-balancer-type-none--load-balancer-subnet-none-)**.
+-  If you create a new cluster, you use **[AksCompute.provisioning_configuration()](/python/api/azureml-core/azureml.core.compute.akscompute?view=azure-ml-py#provisioning-configuration-agent-count-none--vm-size-none--ssl-cname-none--ssl-cert-pem-file-none--ssl-key-pem-file-none--location-none--vnet-resourcegroup-name-none--vnet-name-none--subnet-name-none--service-cidr-none--dns-service-ip-none--docker-bridge-cidr-none--cluster-purpose-none--load-balancer-type-none--load-balancer-subnet-none-)**.
 - If you attach an existing cluster, you use **[AksCompute.attach_configuration()](/python/api/azureml-core/azureml.core.compute.akscompute?view=azure-ml-py#attach-configuration-resource-group-none--cluster-name-none--resource-id-none--cluster-purpose-none-)**. Both return a configuration object that has an **enable_ssl** method.
 
 The **enable_ssl** method can use a certificate that's provided by Microsoft or a certificate that you purchase.
@@ -164,7 +164,7 @@ Next, you must update your DNS to point to the web service.
 
 SSL certificates expire and must be renewed. Typically this happens every year. Use the information in the following sections to update and renew your certificate for models deployed to Azure Kubernetes Service:
 
-### Update a Microsoft generated certificate
+### Update a Microsoft-generated certificate
 
 If the certificate was originally generated by Microsoft (when using the *leaf_domain_label* to create the service), use one of the following examples to update the certificate: