Update how-to-create-data-assets.md

Author: SturgeonMi

articles/machine-learning/how-to-create-data-assets.md

@@ -27,7 +27,7 @@ In this article, you learn how to create a data asset in Azure Machine Learning.
 
 The benefits of creating data assets are:
 
-* You can **share and reuse data** with other members of the team such that they do not need to remember file locations.
+* You can **share and reuse data** with other members of the team such that they don't need to remember file locations.
 
 * You can **seamlessly access data** during model training (on any supported compute type) without worrying about connection strings or data paths.
 
@@ -63,13 +63,13 @@ When you create a data asset in Azure Machine Learning, you'll need to specify a
 
 
 ## Data asset types
- - [**URIs**](#Create a `uri_folder` data asset) - A **U**niform **R**esource **I**dentifier that is a reference to a storage location on your local computer or in the cloud that makes it very easy to access data in your jobs. Azure Machine Learning distinguishes two types of URIs:`uri_file` and `uri_folder`.
+ - [**URIs**](#Create a `uri_folder` data asset) - A **U**niform **R**esource **I**dentifier that is a reference to a storage location on your local computer or in the cloud that makes it easy to access data in your jobs. Azure Machine Learning distinguishes two types of URIs:`uri_file` and `uri_folder`.
 
- - [**MLTable**](#Create a `mltable` data asset) - `MLTable` helps you to abstract the schema definition for tabular data so it is more suitable for complex/changing schema or to be leveraged in automl. If you just want to create an data asset for a job or you want to write your own parsing logic in python you could use `uri_file`, `uri_folder`.
+ - [**MLTable**](#Create a `mltable` data asset) - `MLTable` helps you to abstract the schema definition for tabular data so it is more suitable for complex/changing schema or to be used in AutoML. If you just want to create a data asset for a job or you want to write your own parsing logic in python you could use `uri_file`, `uri_folder`.
 
  The ideal scenarios to use `mltable` are:
  - The schema of your data is complex and/or changes frequently.
- - You only need a subset of data (for example: a sample of rows or files, specific columns, etc).
+ - You only need a subset of data (for example: a sample of rows or files, specific columns, etc.)
  - AutoML jobs requiring tabular data.
 
 If your scenario does not fit the above then it is likely that URIs are a more suitable type.
@@ -223,7 +223,7 @@ To create a File data asset in the Azure Machine Learning studio, use the follow
 - JSON Lines
 - Delta Lake 
 
-Please find more details about what are the abilities we provide via `mltable` in [reference-yaml-mltable](reference-yaml-mltable.md).
+Find more details about what are the abilities we provide via `mltable` in [reference-yaml-mltable](reference-yaml-mltable.md).
 
 In this section, we show you how to create a data asset when the type is an `mltable`.
 
@@ -262,7 +262,7 @@ transformations:
 #version_as_of: Version to be specified for time-travel on the specific Delta Lake data.
 ```
 
-For more transformations availabe in `mltable`, please look into [reference-yaml-mltable](reference-yaml-mltable.md). 
+For more transformations available in `mltable`, please look into [reference-yaml-mltable](reference-yaml-mltable.md). 
 
 
 > [!IMPORTANT]