fix broken links

Author: laujan

articles/ai-services/document-intelligence/how-to-guides/includes/v2-1/python-sdk.md

@@ -57,7 +57,7 @@ Install the client library and create a Python application.
 
 ## Use the Object model
 
-With Document Intelligence, you can create two different client types. The first, `form_recognizer_client`, queries the service to recognize form fields and content. The second, `form_training_client`, creates and manages custom models to improve recognition.
+With Document Intelligence, you can create two different client types. The first—`form_recognizer_client`—queries the service to recognize form fields and content. The second—`form_training_client`—creates and manages custom models to improve recognition.
 
 `form_recognizer_client` provides the following operations:
 
@@ -183,7 +183,7 @@ To analyze invoices from a URL, use the `begin_recognize_invoices_from_url` meth
 
 ## Analyze ID documents
 
-This section demonstrates how to analyze and extract key information from government-issued identification documents, including worldwide passports and U.S. driver's licenses, by using the Document Intelligence prebuilt ID model. For more information about ID document analysis, see the [Document Intelligence ID document model](../../../prebuilt/id-document.md).
+This section demonstrates how to analyze and extract key information from government-issued identification documents such as worldwide passports and U.S. driver's licenses using the prebuilt ID model. For more information about ID document analysis, see the [Document Intelligence ID document model](../../../prebuilt/id-document.md).
 
 To analyze ID documents from a URL, use the `begin_recognize_id_documents_from_url` method.
 
@@ -286,7 +286,7 @@ Document errors: []
 This section demonstrates how to extract key/value information and other content from your custom template types, using models you trained with your own forms.
 
 > [!IMPORTANT]
-> In order to implement this scenario, you must have already trained a model so you can pass its ID into the method operation. See the [Train a model](#train-a-model-without-labels) section.
+> In order to implement this scenario, you must have a trained a model so you can pass its ID into the method operation. See the [Train a model](#train-a-model-without-labels) section.
 
 You use the `begin_recognize_custom_forms_from_url` method. The returned value is a collection of `RecognizedForm` objects. There's one object for each page in the submitted document. The following code prints the analysis results to the console. It prints each recognized field and corresponding value, along with a confidence score.
 
@@ -406,9 +406,11 @@ Similarly, `logging_enable` can enable detailed logging for a single operation,
 ## REST samples on GitHub
 
 - Extract text, selection marks, and table structure from documents: [Extract layout data - Python](https://github.com/Azure-Samples/cognitive-services-quickstart-code/blob/master/python/FormRecognizer/rest/python-layout.md)
-- Train custom models and extract custom form data:
+- Train a custom model and extract custom form data:
+
   - [Train without labels - Python](https://github.com/Azure-Samples/cognitive-services-quickstart-code/blob/master/python/FormRecognizer/rest/python-train-extract.md)
   - [Train with labels - Python](https://github.com/Azure-Samples/cognitive-services-quickstart-code/blob/master/python/FormRecognizer/rest/python-labeled-data.md)
+  
 - Extract data from invoices: [Extract invoice data - Python](https://github.com/Azure-Samples/cognitive-services-quickstart-code/blob/master/python/FormRecognizer/rest/python-invoices.md)
 - Extract data from sales receipts: [Extract receipt data - Python](https://github.com/Azure-Samples/cognitive-services-quickstart-code/blob/master/python/FormRecognizer/rest/python-receipts.md)
 - Extract data from business cards: [Extract business card data - Python](https://github.com/Azure-Samples/cognitive-services-quickstart-code/blob/master/python/FormRecognizer/rest/python-business-cards.md)