Update how-to-prepare-datasets-for-automl-images.md

Author: jborsecnik

articles/machine-learning/how-to-prepare-datasets-for-automl-images.md

@@ -77,9 +77,9 @@ my_training_data_input = Input(
 ### Using pre-labeled training data from local machine
 If you have previously labeled data that you would like to use to train your model, you will first need to upload the images to the default Azure Blob Storage of your Azure ML Workspace and register it as a [data asset](how-to-create-data-assets.md). 
 
-Below scripts uploads the image data on your local machine at path "./data/odFridgeObjects" to datastore in Azure Blob Storage. Thereafter, it creates a new data asset with the name "fridge-items-images-object-detection" in your Azure ML Workspace. 
+The following script uploads the image data on your local machine at path "./data/odFridgeObjects" to datastore in Azure Blob Storage. It then creates a new data asset with the name "fridge-items-images-object-detection" in your Azure ML Workspace. 
 
-If there already exists a data asset with name "fridge-items-images-object-detection" in your Azure ML Workspace, then it will update version number of the data asset and point it to the new location where the image data uploaded.
+If there already exists a data asset with the name "fridge-items-images-object-detection" in your Azure ML Workspace, it will update the version number of the data asset and point it to the new location where the image data uploaded.
 
 # [Azure CLI](#tab/cli)
 [!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
@@ -107,7 +107,7 @@ az ml data create -f [PATH_TO_YML_FILE] --workspace-name [YOUR_AZURE_WORKSPACE]
 [!Notebook-python[] (~/azureml-examples-v2samplesreorg/sdk/python/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items/automl-image-object-detection-task-fridge-items.ipynb?name=upload-data)]
 ---
 
-If you already have your data present in an existing datastore and want to create data asset out of it, you can do so by providing path to the data in datastore, instead of providing path of your local machine. Update the code in [above section](how-to-prepare-datasets-for-automl-images.md#using-pre-labeled-training-data-from-local-machine) with below snippet.
+If you already have your data present in an existing datastore and want to create a data asset out of it, you can do so by providing the path to the data in the datastore, instead of providing the path of your local machine. Update the code [above](how-to-prepare-datasets-for-automl-images.md#using-pre-labeled-training-data-from-local-machine) with the following snippet.
 
 # [Azure CLI](#tab/cli)
 [!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]