Update links.

Author: Sherry Yang

learn-pr/wwl-data-ai/introduction-information-extraction/includes/3-vision-extraction.md

@@ -7,6 +7,6 @@ Computer vision is made possible by machine learning models that are trained to
 **Optical Character Recognition (OCR)** helps computers recognize that an element in an image contains text. OCR is the foundation of processing text in images and uses machine learning models that are trained to recognize individual shapes as letters, numerals, punctuation, or other elements of text. Much of the early work on implementing this kind of capability was performed by postal services to support automatic sorting of mail based on postal codes. Since then, the state-of-the-art for reading text has moved on, and we have models that detect printed or handwritten text in an image and digitize it line-by-line and word-by-word.
 
 > [!NOTE]
-> The machine learning concepts associated with vision are covered in-depth in [Introduction to computer vision concepts](https://learn.microsoft.com/en-us/training/modules/analyze-images-computer-vision/).  
+> The machine learning concepts associated with vision are covered in-depth in [Introduction to computer vision concepts](/training/modules/analyze-images-computer-vision/).  
 
 Next, let's see how data is extracted from forms with techniques that build upon OCR. 

learn-pr/wwl-data-ai/introduction-information-extraction/includes/5-multimodal-extraction.md

@@ -5,16 +5,16 @@ The orchastration of techniques can include vision and document intelligence, an
 - **Natural language processing** can be used to find key phrases, entities, sentiment, etc. in written or spoken language.
 
 > [!NOTE] 
-> The machine learning concepts associated with NLP are covered in-depth in [Introduction to natural language processing concepts](https://learn.microsoft.com/training/modules/analyze-text-with-text-analytics-service).
+> The machine learning concepts associated with NLP are covered in-depth in [Introduction to natural language processing concepts](/training/modules/analyze-text-with-text-analytics-service).
 
 - **Speech recognition** takes the spoken word and converts it into data that can be processed - often by transcribing it into text. The spoken words can be in the form of a recorded voice in an audio file, or live audio from a microphone. 
 
 > [!NOTE] 
-> Speech recognition is covered in [Get started with speech on Azure](https://learn.microsoft.com/training/modules/recognize-synthesize-speech)
+> Speech recognition is covered in [Get started with speech on Azure](/training/modules/recognize-synthesize-speech)
 
 - **Generative AI** can add to the data extraction process by allowing users to identify their own fields and field descriptions. It can be particularly useful when dealing with unstructured content. One example is the user-added *field* of "summary". The *value* associated with the field must be generated based on the data in the content.
 
 >[!NOTE]
-> Generative AI concepts are covered in-depth in [Introduction to generative AI on Azure](https://learn.microsoft.com/training/modules/fundamentals-azure-ai-services/)
+> Generative AI concepts are covered in-depth in [Introduction to generative AI on Azure](/training/modules/fundamentals-azure-ai-services/)
 
 ![Screenshot of the possible components of multimodal information extraction.](../media/component-overview.png)

learn-pr/wwl-data-ai/introduction-information-extraction/includes/8-summary.md

@@ -3,4 +3,4 @@ In this module you learned how modern organizations can process content using AI
 
 ## Learn more
 
-Apply these concepts in [Get started with AI-powered information extraction in Azure](https://learn.microsoft.com/training/modules/ai-information-extraction/).
+Apply these concepts in [Get started with AI-powered information extraction in Azure](/training/modules/ai-information-extraction/).