Update README.md

5-Data-Science-In-Cloud/18-Low-Code/README.md

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 ### 1.1 What is Azure Machine Learning?
 Data scientists expend a lot of effort exploring and pre-processing data, and trying various types of model-training algorithms to produce accurate models. These tasks are time consuming, and often make inefficient use of expensive compute hardware.
 
-[Azure ML](https://docs.microsoft.com/azure/machine-learning/overview-what-is-azure-machine-learning) is a cloud-based platform for building and operating machine learning solutions in Azure. It includes a wide range of features and capabilities that help data scientists prepare data, train models, publish predictive services, and monitor their usage. Most importantly, it helps them to increase their efficiency by automating many of the time-consuming tasks associated with training models; and it enables them to use cloud-based compute resources that scale effectively, to handle large volumes of data while incurring costs only when actually used.
+[Azure ML](https://docs.microsoft.com/azure/machine-learning/overview-what-is-azure-machine-learning?WT.mc_id=academic-40229-cxa&ocid=AID3041109) is a cloud-based platform for building and operating machine learning solutions in Azure. It includes a wide range of features and capabilities that help data scientists prepare data, train models, publish predictive services, and monitor their usage. Most importantly, it helps them to increase their efficiency by automating many of the time-consuming tasks associated with training models; and it enables them to use cloud-based compute resources that scale effectively, to handle large volumes of data while incurring costs only when actually used.
 
 Azure ML provides all the tools developers and data scientists need for their machine learning workflows. These include:
 
@@ -98,7 +98,7 @@ Once you have the dataset, we can start the project in Azure.
 
 ## 2. Low code/No code training of a model in Azure ML Studio
 ### 2.1 Create an Azure ML workspace
-To train a model in Azure ML you first need to create an Azure ML workspace. The workspace is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning. The workspace keeps a history of all training runs, including logs, metrics, output, and a snapshot of your scripts. You use this information to determine which training run produces the best model. [Learn more](https://docs.microsoft.com/azure/machine-learning/concept-workspace)
+To train a model in Azure ML you first need to create an Azure ML workspace. The workspace is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning. The workspace keeps a history of all training runs, including logs, metrics, output, and a snapshot of your scripts. You use this information to determine which training run produces the best model. [Learn more](https://docs.microsoft.com/azure/machine-learning/concept-workspace?WT.mc_id=academic-40229-cxa&ocid=AID3041109)
 
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 ### 2.4 Low code/No Code training with AutoML 
 
 Traditional machine learning model development is resource-intensive, requires significant domain knowledge and time to produce and compare dozens of models. 
-Automated machine learning (AutoML), is the process of automating the time-consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity, all while sustaining model quality. It reduces the time it takes to get production-ready ML models, with great ease and efficiency. [Learn more](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml)
+Automated machine learning (AutoML), is the process of automating the time-consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity, all while sustaining model quality. It reduces the time it takes to get production-ready ML models, with great ease and efficiency. [Learn more](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml?WT.mc_id=academic-40229-cxa&ocid=AID3041109)
 
 1. In the [Azure ML workspace](https://ml.azure.com/) that we created earlier click on "Automated ML" in the left menu and select the dataset you just uploaded. Click Next.
 
@@ -355,7 +355,7 @@ Look closely at the model explainations and details that AutoML generated for th
 
 In this lesson, you learned how to train, deploy and consume a model to predict heart failure risk in a Low code/No code fashion in the cloud. If you have not done it yet, dive deeper into the model explainations that AutoML generated for the top models and try to understand why the best model is better than others.
 
-You can go further into Low code/No code AutoML by reading this [documentation](https://docs.microsoft.com/azure/machine-learning/tutorial-first-experiment-automated-ml).
+You can go further into Low code/No code AutoML by reading this [documentation](https://docs.microsoft.com/azure/machine-learning/tutorial-first-experiment-automated-ml?WT.mc_id=academic-40229-cxa&ocid=AID3041109).
 
 
 ## Assignment