Fixing spelling issues

Author: luisquintanilla

articles/machine-learning/how-to-deploy-azure-kubernetes-service.md

@@ -9,7 +9,7 @@ ms.topic: conceptual
 ms.author: jordane
 author: jpe316
 ms.reviewer: larryfr
-ms.date: 12/27/2019
+ms.date: 01/16/2020
 ---
 
 # Deploy a model to an Azure Kubernetes Service cluster
@@ -104,7 +104,7 @@ For more information on the classes, methods, and parameters used in this exampl
 az ml computetarget create aks -n myaks
 ```
 
-For more information, see the [az ml computetarget create ask](https://docs.microsoft.com/cli/azure/ext/azure-cli-ml/ml/computetarget/create?view=azure-cli-latest#ext-azure-cli-ml-az-ml-computetarget-create-aks) reference.
+For more information, see the [`az ml computetarget create aks`](https://docs.microsoft.com/cli/azure/ext/azure-cli-ml/ml/computetarget/create?view=azure-cli-latest#ext-azure-cli-ml-az-ml-computetarget-create-aks) reference.
 
 ## Attach an existing AKS cluster
 
@@ -178,7 +178,7 @@ To attach the existing cluster to your workspace, use the following command. Rep
 az ml computetarget attach aks -n myaks -i aksresourceid -g myresourcegroup -w myworkspace
 ```
 
-For more information, see the [az ml computetarget attach aks](https://docs.microsoft.com/cli/azure/ext/azure-cli-ml/ml/computetarget/attach?view=azure-cli-latest#ext-azure-cli-ml-az-ml-computetarget-attach-aks) reference.
+For more information, see the [`az ml computetarget attach aks`](https://docs.microsoft.com/cli/azure/ext/azure-cli-ml/ml/computetarget/attach?view=azure-cli-latest#ext-azure-cli-ml-az-ml-computetarget-attach-aks) reference.
 
 ## Deploy to AKS
 

articles/machine-learning/how-to-set-up-training-targets.md

@@ -9,7 +9,7 @@ ms.reviewer: sgilley
 ms.service: machine-learning
 ms.subservice: core
 ms.topic: conceptual
-ms.date: 12/27/2019
+ms.date: 01/16/2020
 ms.custom: seodec18
 ---
 # Set up and use compute targets for model training 
@@ -48,7 +48,7 @@ Learn more about [submitting experiments](#submit) at the end of this article.
 
 ## What's an estimator?
 
-To facilitate model training using popular frameworks, the Azure Machine Learning Python SDK provides an alternative higher-level abstraction, the estimator class.  This class allows you to easily construct run configurations. You can create and use a generic [Estimator](https://docs.microsoft.com/python/api/azureml-train-core/azureml.train.estimator?view=azure-ml-py) to submit training scripts that use any learning framework you choose (such as scikit-learn). We recommend using an estimator for training as it automatically contructs embedded objects like an environment or RunConfiguration objects for you. If you wish to have more control over how these objects are created and specfify what packages to install for your experiement run, follow [these steps](#amlcompute) to submit your training experiments using a RunConfiguration object on an Azure Machine Learning Compute.
+To facilitate model training using popular frameworks, the Azure Machine Learning Python SDK provides an alternative higher-level abstraction, the estimator class.  This class allows you to easily construct run configurations. You can create and use a generic [Estimator](https://docs.microsoft.com/python/api/azureml-train-core/azureml.train.estimator?view=azure-ml-py) to submit training scripts that use any learning framework you choose (such as scikit-learn). We recommend using an estimator for training as it automatically constructs embedded objects like an environment or RunConfiguration objects for you. If you wish to have more control over how these objects are created and specify what packages to install for your experiment run, follow [these steps](#amlcompute) to submit your training experiments using a RunConfiguration object on an Azure Machine Learning Compute.
 
 For PyTorch, TensorFlow, and Chainer tasks, Azure Machine Learning also provides respective [PyTorch](https://docs.microsoft.com/python/api/azureml-train-core/azureml.train.dnn.pytorch?view=azure-ml-py), [TensorFlow](https://docs.microsoft.com/python/api/azureml-train-core/azureml.train.dnn.tensorflow?view=azure-ml-py), and [Chainer](https://docs.microsoft.com/python/api/azureml-train-core/azureml.train.dnn.chainer?view=azure-ml-py) estimators to simplify using these frameworks.