paths fixed

Author: brown9804

0_Azure/2_AzureAnalytics/0_Fabric/demos/17_Overview.md

@@ -271,12 +271,12 @@ Click [here](https://github.com/brown9804/MicrosoftCloudEssentialsHub/blob/main/
 
 ## Fabric: Highlights into AI/LLMs
 
-Click [here](https://github.com/brown9804/MicrosoftCloudEssentialsHub/tree/main/0_Azure/2_AzureAnalytics/0_Fabric/demos/13_FabricAI_LLMs#configure-azure-openai-service) to see a quick guide and demo.
+Click [here](https://github.com/brown9804/MicrosoftCloudEssentialsHub/blob/main/0_Azure/2_AzureAnalytics/0_Fabric/demos/13_FabricAI_LLMs#configure-azure-openai-service) to see a quick guide and demo.
 
-- Example of [how to integrate Azure OpenAI with Fabric](https://github.com/brown9804/MicrosoftCloudEssentialsHub/tree/copilot_studio_demo/0_Azure/2_AzureAnalytics/0_Fabric/demos/13_FabricAI_LLMs#configure-azure-openai-service): Call a deployed model and request information.
-- [Basic Usage of LangChain Transformer](https://github.com/brown9804/MicrosoftCloudEssentialsHub/tree/main/0_Azure/2_AzureAnalytics/0_Fabric/demos/13_FabricAI_LLMs#basic-usage-of-langchain-transformer): Create a prompt template, set up an LLMChain, and configure the transformer to execute the processing chain.
-- Example of [Using LangChain for Large Scale Literature Review](https://github.com/brown9804/MicrosoftCloudEssentialsHub/tree/main/0_Azure/2_AzureAnalytics/0_Fabric/demos/13_FabricAI_LLMs#using-langchain-for-large-scale-literature-review): This example is around extracting content from PDFs linked in arXiv papers and generating prompts for extracting specific information.
-- [Machine Learning Integration with Microsoft Fabric](https://github.com/brown9804/MicrosoftCloudEssentialsHub/tree/main/0_Azure/2_AzureAnalytics/0_Fabric/demos/13_FabricAI_LLMs#machine-learning-integration-with-microsoft-fabric): Shows how to train and register machine learning models using Microsoft Fabric's native integration with the MLflow framework. This includes logging trained models, hyperparameters, and evaluation metrics. It also shows how to compare and filter machine learning models using MLflow, with an example using RandomForestRegressor.
+- Example of [how to integrate Azure OpenAI with Fabric](https://github.com/brown9804/MicrosoftCloudEssentialsHub/blob/main/0_Azure/2_AzureAnalytics/0_Fabric/demos/13_FabricAI_LLMs#configure-azure-openai-service): Call a deployed model and request information.
+- [Basic Usage of LangChain Transformer](https://github.com/brown9804/MicrosoftCloudEssentialsHub/blob/main/0_Azure/2_AzureAnalytics/0_Fabric/demos/13_FabricAI_LLMs#basic-usage-of-langchain-transformer): Create a prompt template, set up an LLMChain, and configure the transformer to execute the processing chain.
+- Example of [Using LangChain for Large Scale Literature Review](https://github.com/brown9804/MicrosoftCloudEssentialsHub/blob/main/0_Azure/2_AzureAnalytics/0_Fabric/demos/13_FabricAI_LLMs#using-langchain-for-large-scale-literature-review): This example is around extracting content from PDFs linked in arXiv papers and generating prompts for extracting specific information.
+- [Machine Learning Integration with Microsoft Fabric](https://github.com/brown9804/MicrosoftCloudEssentialsHub/blob/main/0_Azure/2_AzureAnalytics/0_Fabric/demos/13_FabricAI_LLMs#machine-learning-integration-with-microsoft-fabric): Shows how to train and register machine learning models using Microsoft Fabric's native integration with the MLflow framework. This includes logging trained models, hyperparameters, and evaluation metrics. It also shows how to compare and filter machine learning models using MLflow, with an example using RandomForestRegressor.
 
 ## Writing SQL: SQL Analytics Endpoint