Merge pull request #9 from aahill/patch-92

Update fail-over.md

Author: magrefaat

articles/cognitive-services/language-service/custom-classification/fail-over.md

@@ -132,7 +132,7 @@ Use the url from the `resultUrl` key in the body to view the exported assets fro
 
 ### Get export results
 
-Submit a **GET** request using the `{RESULT-URL}` you recieved from the previous step to view the results of the export job.
+Submit a **GET** request using the `{RESULT-URL}` you received from the previous step to view the results of the export job.
 
 #### Headers
 
@@ -330,7 +330,7 @@ Use the following header to authenticate your request.
 
 ## Deploy your model
 
-This is te step where you make your trained model available form consumption via the [runtime prediction API](https://aka.ms/ct-runtime-swagger). 
+This is the step where you make your trained model available form consumption via the [runtime prediction API](https://aka.ms/ct-runtime-swagger). 
 
 > [!TIP]
 > Use the same deployment name as your primary project for easier maintenance and minimal changes to your system to handle redirecting your traffic.

articles/cognitive-services/language-service/custom-classification/how-to/train-model.md

@@ -30,7 +30,7 @@ See the [application development lifecycle](../overview.md#project-development-l
 
 ## Data split
 
-Before starting the training process, files in your dataset are divided into three groups at random:
+Before you start the training process, files in your dataset are divided into three groups at random:
 
 * The **training set** contains 80% of the files in your dataset. It is the main set that is used to train the model.
 

articles/cognitive-services/language-service/custom-classification/includes/quickstarts/rest-api.md

@@ -130,7 +130,7 @@ This request will return an error if:
 
 ## Start training your model
 
-After your project has been created, you can begin training a cusom text classification model. Create a **POST** request using the following URL, headers, and JSON body to start training a custom text classification model.
+After your project has been created, you can begin training a custom text classification model. Create a **POST** request using the following URL, headers, and JSON body to start training a custom text classification model.
 
 ### Request URL