Merge pull request #193 from Azure-Samples/pafarley-updates

fix term

Author: PatrickFarley

python/ComputerVision/REST/python-analyze.md

@@ -2,7 +2,7 @@
 
 In this quickstart, you'll analyze a remotely stored image to extract visual features using the Computer Vision REST API. With the [Analyze Image](https://westcentralus.dev.cognitive.microsoft.com/docs/services/computer-vision-v3-1-ga/operations/56f91f2e778daf14a499f21b) method, you can extract visual features based on image content.
 
-You can run this quickstart in a step-by step fashion using a Jupyter notebook on [MyBinder](https://mybinder.org). To launch Binder, select the following button:
+You can run this quickstart in a step-by step fashion using a Jupyter Notebook on [MyBinder](https://mybinder.org). To launch Binder, select the following button:
 
 [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/Microsoft/cognitive-services-notebooks/master?filepath=VisionAPI.ipynb)
 
@@ -30,7 +30,7 @@ To create and run the sample, do the following steps:
 
 ```python
 import requests
-# If you are using a Jupyter notebook, uncomment the following line.
+# If you are using a Jupyter Notebook, uncomment the following line.
 # %matplotlib inline
 import matplotlib.pyplot as plt
 import json

python/ComputerVision/REST/python-disk.md

@@ -3,7 +3,7 @@
 
 In this quickstart, you'll analyze a locally stored image to extract visual features using the Computer Vision REST API. With the [Analyze Image](https://westcentralus.dev.cognitive.microsoft.com/docs/services/computer-vision-v3-1-ga/operations/56f91f2e778daf14a499f21b) method, you can extract visual features based on image content.
 
-You can run this quickstart in a step-by step fashion using a Jupyter notebook on [MyBinder](https://mybinder.org). To launch Binder, select the following button:
+You can run this quickstart in a step-by step fashion using a Jupyter Notebook on [MyBinder](https://mybinder.org). To launch Binder, select the following button:
 
 [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/Microsoft/cognitive-services-notebooks/master?filepath=VisionAPI.ipynb)
 
@@ -33,7 +33,7 @@ To create and run the sample, do the following steps:
 import os
 import sys
 import requests
-# If you are using a Jupyter notebook, uncomment the following line.
+# If you are using a Jupyter Notebook, uncomment the following line.
 # %matplotlib inline
 import matplotlib.pyplot as plt
 from PIL import Image

python/ComputerVision/REST/python-domain.md

@@ -3,7 +3,7 @@
 
 In this quickstart, you'll use a domain model to identify landmarks or, optionally, celebrities in a remotely stored image using the Computer Vision REST API. With the [Recognize Domain Specific Content](https://westcentralus.dev.cognitive.microsoft.com/docs/services/computer-vision-v3-1-ga/operations/56f91f2e778daf14a499f311) method, you can apply a domain-specific model to recognize content within an image.
 
-You can run this quickstart in a step-by step fashion using a Jupyter notebook on [MyBinder](https://mybinder.org). To launch Binder, select the following button:
+You can run this quickstart in a step-by step fashion using a Jupyter Notebook on [MyBinder](https://mybinder.org). To launch Binder, select the following button:
 
 [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/Microsoft/cognitive-services-notebooks/master?filepath=VisionAPI.ipynb)
 
@@ -30,7 +30,7 @@ To create and run the landmark sample, do the following steps:
 import os
 import sys
 import requests
-# If you are using a Jupyter notebook, uncomment the following line.
+# If you are using a Jupyter Notebook, uncomment the following line.
 # %matplotlib inline
 import matplotlib.pyplot as plt
 from PIL import Image
@@ -112,7 +112,7 @@ To create and run the landmark sample, do the following steps:
 
 ```python
 import requests
-# If you are using a Jupyter notebook, uncomment the following line.
+# If you are using a Jupyter Notebook, uncomment the following line.
 # %matplotlib inline
 import matplotlib.pyplot as plt
 from PIL import Image

python/ComputerVision/REST/python-hand-text.md

@@ -8,7 +8,7 @@ In this quickstart, you'll extract printed and handwritten text from an image us
 
 ---
 
-You can run this quickstart in a step-by step fashion using a Jupyter notebook on [MyBinder](https://mybinder.org). To launch Binder, select the following button:
+You can run this quickstart in a step-by step fashion using a Jupyter Notebook on [MyBinder](https://mybinder.org). To launch Binder, select the following button:
 
 [![The launch Binder button](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/Microsoft/cognitive-services-notebooks/master?filepath=VisionAPI.ipynb)
 
@@ -37,7 +37,7 @@ import os
 import sys
 import requests
 import time
-# If you are using a Jupyter notebook, uncomment the following line.
+# If you are using a Jupyter Notebook, uncomment the following line.
 # %matplotlib inline
 import matplotlib.pyplot as plt
 from matplotlib.patches import Polygon

python/ComputerVision/REST/python-print-text.md

@@ -7,7 +7,7 @@
 
 In this quickstart, you will extract printed text with optical character recognition (OCR) from an image using the Computer Vision REST API. With the [OCR](https://westcentralus.dev.cognitive.microsoft.com/docs/services/computer-vision-v3-1-ga/operations/56f91f2e778daf14a499f20d) method, you can detect printed text in an image and extract recognized characters into a machine-usable character stream.
 
-You can run this quickstart in a step-by step fashion using a Jupyter notebook on [MyBinder](https://mybinder.org). To launch Binder, select the following button:
+You can run this quickstart in a step-by step fashion using a Jupyter Notebook on [MyBinder](https://mybinder.org). To launch Binder, select the following button:
 
 [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/Microsoft/cognitive-services-notebooks/master?filepath=VisionAPI.ipynb)
 
@@ -35,7 +35,7 @@ To create and run the sample, do the following steps:
 import os
 import sys
 import requests
-# If you are using a Jupyter notebook, uncomment the following line.
+# If you are using a Jupyter Notebook, uncomment the following line.
 # %matplotlib inline
 import matplotlib.pyplot as plt
 from matplotlib.patches import Rectangle

python/ComputerVision/REST/python-thumb.md

@@ -20,7 +20,7 @@ To create and run the sample, copy the following code into the code editor.
 import os
 import sys
 import requests
-# If you are using a Jupyter notebook, uncomment the following lines.
+# If you are using a Jupyter Notebook, uncomment the following lines.
 # %matplotlib inline
 # import matplotlib.pyplot as plt
 from PIL import Image
@@ -73,7 +73,7 @@ A successful response is returned as binary data which represents the image data
 
 ## Run in Jupyter (optional)
 
-You can optionally run this quickstart in a step-by step fashion using a Jupyter notebook on [MyBinder](https://mybinder.org). To launch Binder, select the following button:
+You can optionally run this quickstart in a step-by step fashion using a Jupyter Notebook on [MyBinder](https://mybinder.org). To launch Binder, select the following button:
 
 [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/Microsoft/cognitive-services-notebooks/master?filepath=VisionAPI.ipynb)