Merge pull request #87027 from v-rihow/use-geo-ai-dsvm

edit pass: use-geo-ai-dsvm

Author: ShawnJackson

articles/machine-learning/data-science-virtual-machine/use-geo-ai-dsvm.md

@@ -1,7 +1,7 @@
 ---
 title: Using the Geo Artificial Intelligence Data Science Virtual Machine  - Azure | Microsoft Docs
 description: Learn how to use the Geo AI Data Science Virtual Machine to analyze data and build models based on geospatial data.
-keywords: deep learning, AI, data science tools, data science virtual machine, Geospatial analytics
+keywords: deep learning, AI, data science tools, data science virtual machine, geospatial analytics
 services: machine-learning
 documentationcenter: ''
 author: vijetajo
@@ -23,29 +23,29 @@ ms.author: vijetaj
 
 # Using the Geo Artificial Intelligence Data Science Virtual Machine
 
-Use the Geo AI Data Science VM to fetch data for analysis, perform data wrangling, and build models for AI applications that consume geospatial information. Once you have provisioned your Geo AI Data Science VM and signed into ArcGIS Pro with your ArcGIS account, you can start interacting with ArcGIS desktop and ArcGis online. You can also access ArcGIS from Python interfaces and an R language bridge pre-configured on the Geo-Data Science VM. To build rich AI applications, combine it with the machine learning and deep learning frameworks and other data science software available on the Data Science VM.  
+Use the Geo AI Data Science VM to fetch data for analysis, perform data wrangling, and build models for AI applications that consume geospatial information. After you've provisioned your Geo AI Data Science VM and signed in to ArcGIS Pro through your ArcGIS account, you can start interacting with ArcGIS desktop and ArcGIs online. You can also access ArcGIS from Python interfaces and an R language bridge that's preconfigured on the Geo-Data Science VM. To build rich AI applications, combine the Geo-Data Science VM with the machine-learning and deep-learning frameworks and other data science software that are available on it.  
 
 
 ## Configuration details
 
-The Python library, [arcpy](https://pro.arcgis.com/en/pro-app/arcpy/main/arcgis-pro-arcpy-reference.htm), which is used to interface with ArcGIS is installed in the global root conda environment of the Data Science VM that is found at ```c:\anaconda```. 
+The Python library, [arcpy](https://pro.arcgis.com/en/pro-app/arcpy/main/arcgis-pro-arcpy-reference.htm), which is used to interface with ArcGIS, is installed in the global root conda environment of the Data Science VM that's found at ```c:\anaconda```.
 
-- If you are running Python in a command prompt, run ```activate``` to activate into the conda root Python environment. 
-- If you are using an IDE or Jupyter notebook, you can select the environment or kernel to ensure you are in the correct conda environment. 
+- If you're running Python at a command prompt, run ```activate``` to activate into the conda root Python environment.
+- If you're using an IDE or Jupyter notebook, you can select the environment or kernel to make sure you're in the correct conda environment.
 
-The R-bridge to ArcGIS is installed as an R library named [arcgisbinding](https://github.com/R-ArcGIS/r-bridge) in the main Microsoft R server standalone instance located at ```C:\Program Files\Microsoft\ML Server\R_SERVER```. Visual Studio, RStudio, and Jupyter are already pre-configured to use this R environment and will have access to the ```arcgisbinding``` R library. 
+The R bridge to ArcGIS is installed as an R library named [arcgisbinding](https://github.com/R-ArcGIS/r-bridge) in the main Microsoft Machine Learning Server standalone instance that's located at ```C:\Program Files\Microsoft\ML Server\R_SERVER```. Visual Studio, RStudio, and Jupyter are already preconfigured to use this R environment and will have access to the ```arcgisbinding``` R library. 
 
 
 ## Geo AI Data Science VM samples
 
-In addition to the ML and deep learning framework-based samples from the base Data Science VM, a set of geospatial samples is also provided as part of the Geo AI Data Science VM. These samples can help you jump-start your development of AI applications using Geospatial data and the ArcGIS software. 
+In addition to the machine-learning and deep-learning framework-based samples from the base Data Science VM, a set of geospatial samples is also provided as part of the Geo AI Data Science VM. These samples can help you jump-start your development of AI applications by using geospatial data and the ArcGIS software:
 
 
-1. [Getting stated with Geospatial analytics with Python](https://github.com/Azure/DataScienceVM/blob/master/Notebooks/ArcGIS/Python%20walkthrough%20ArcGIS%20Data%20analysis%20and%20ML.ipynb): An introductory sample showing how to work with Geospatial data using the Python interface to ArcGIS provided by the [arcpy](https://pro.arcgis.com/en/pro-app/arcpy/main/arcgis-pro-arcpy-reference.htm) library. It also shows how you can combine traditional machine learning with geospatial data and visualize the result on a map in ArcGIS. 
+1. [Getting started with geospatial analytics with Python](https://github.com/Azure/DataScienceVM/blob/master/Notebooks/ArcGIS/Python%20walkthrough%20ArcGIS%20Data%20analysis%20and%20ML.ipynb): An introductory sample showing how to work with geospatial data through the Python interface to ArcGIS is provided by the [arcpy](https://pro.arcgis.com/en/pro-app/arcpy/main/arcgis-pro-arcpy-reference.htm) library. It also shows how to combine traditional machine learning with geospatial data and then visualize the result on a map in ArcGIS.
 
-2. [Getting stated with Geospatial analytics with R](https://github.com/Azure/DataScienceVM/blob/master/Notebooks/ArcGIS/R%20walkthrough%20ArcGIS%20Data%20analysis%20and%20ML.ipynb): An introductory sample that shows how to work with Geospatial data using the R interface to ArcGIS provided by the [arcgisbinding](https://github.com/R-ArcGIS/r-bridge) library. 
+2. [Getting started with geospatial analytics with R](https://github.com/Azure/DataScienceVM/blob/master/Notebooks/ArcGIS/R%20walkthrough%20ArcGIS%20Data%20analysis%20and%20ML.ipynb): An introductory sample that shows how to work with geospatial data by using the R interface to ArcGIS that's provided by the [arcgisbinding](https://github.com/R-ArcGIS/r-bridge) library. 
 
-3. [Pixel-level land use classification](https://github.com/Azure/pixel_level_land_classification): A tutorial that illustrates how to create a deep neural network model that accepts an aerial image as input and returns a land-cover label. Examples of land-cover labels are "forested" or "water." The model returns such a label for every pixel in the image. The model is built using Microsoft's open-source [Cognitive Toolkit (CNTK)](https://www.microsoft.com/en-us/cognitive-toolkit/) deep learning framework. 
+3. [Pixel-level land use classification](https://github.com/Azure/pixel_level_land_classification): A tutorial that illustrates how to create a deep neural network model that accepts an aerial image as input and returns a land-cover label. Examples of land-cover labels are *forested* and *water*. The model returns such a label for every pixel in the image. The model is built by using the Microsoft open-source [Cognitive Toolkit (CNTK)](https://www.microsoft.com/en-us/cognitive-toolkit/) deep learning framework.
 
 
 ## Next steps