Fix an Acrolinx bug . . .

Author: fbsolo-ms1

articles/machine-learning/concept-data.md

@@ -64,7 +64,7 @@ A URI (storage location) can reference a file, a folder, or a data table. A mach
 |---------|---------|---------|---------|---------|
 |**File**<br>Reference a single file     |    `uri_file`     |   `FileDataset`      |       Read/write a single file - the file can have any format.   |  A type new to V2 APIs. In V1 APIs, files always mapped to a folder on the compute target filesystem; this mapping required an `os.path.join`. In V2 APIs, the single file is mapped. This way, you can refer to that location in your code.   |
 |**Folder**<br> Reference a single folder     |     `uri_folder`    |   `FileDataset`      |  You must read/write a folder of parquet/CSV files into Pandas/Spark.<br><br>Deep-learning with images, text, audio, video files located in a folder.       | In V1 APIs, `FileDataset` had an associated engine that could take a file sample from a folder. In V2 APIs, a folder is a simple mapping to the compute target filesystem. |
-|**Table**<br> Reference a data table    |   `mltable`      |     `TabularDataset`    |    You have a complex schema subject to frequent changes, or you need a subset of large tabular data.<br><br>AutoML with Tables.     | In V1 APIs, the Azure Machine Learning back-end stored the data materialization blueprint. As a result, `TabularDataset` only worked if you had an Azure Machine Learning workspace. `mltable` stores the data materialization blueprint in *your* storage. This storage location means you can use it *disconnected to AzureML* - for example, locally and on-premises. In V2 APIs, it's easier to transition from local to remote jobs. For more information, visit [Working with tables in Azure Machine Learning](how-to-mltable.md). |
+|**Table**<br> Reference a data table    |   `mltable`      |     `TabularDataset`    |    You have a complex schema subject to frequent changes, or you need a subset of large tabular data.<br><br>AutoML with Tables.     | In V1 APIs, the Azure Machine Learning back-end stored the data materialization blueprint. As a result, `TabularDataset` only worked if you had an Azure Machine Learning workspace. `mltable` stores the data materialization blueprint in *your* storage. This storage location means you can use it *disconnected to Azure Machine Learning* - for example, locally and on-premises. In V2 APIs, it's easier to transition from local to remote jobs. For more information, visit [Working with tables in Azure Machine Learning](how-to-mltable.md). |
 
 ## URI
 A Uniform Resource Identifier (URI) represents a storage location on your local computer, Azure storage, or a publicly available http(s) location. These examples show URIs for different storage options: