minor update

Author: Soumya Das

samples/04_gis_analysts_data_scientists/Multi_class_change_detection_using_segmentation_models.ipynb

@@ -100,7 +100,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For this use case, we have landsat-7 imageries and a classified change map with class information between two time periods i.e. from 2001 to 2016, collected over Egypt with spatial resolution of 30 m. We will export the data in “Classified_tiles” metadata format available in the `Export Training Data For Deep Learning` tool. The tool is available in [ArcGIS Pro](https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview) and [ArcGIS Image Server](https://www.esri.com/en-us/arcgis/products/arcgis-image-server).\n",
+    "For this use case, we have landsat-7 imageries and a classified change map with class information between two time periods i.e. from 2001 to 2016, collected over Egypt with spatial resolution of 30 m. We will export the data in “Classified Tiles” metadata format available in the `Export Training Data For Deep Learning` tool. The tool is available in [ArcGIS Pro](https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview) and [ArcGIS Image Server](https://www.esri.com/en-us/arcgis/products/arcgis-image-server).\n",
     "\n",
     "For the model to identify changes between imageries, we will build a composite imagery from two different time periods using the [composite](https://pro.arcgis.com/en/pro-app/latest/tool-reference/data-management/composite-bands.htm) tool in [ArcGIS Pro](https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview). It will be provided as `Input Raster` to the model, and the change map will be provided as labels.\n",
     "\n",
@@ -536,7 +536,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It is a good practice to visualize the results obtained from the model with the actual ground truth data. The show_results function picks random samples and visualize the ground truth and model predictions, side by side. This enables us to preview the results of the model within the notebook."
+    "It is a good practice to visualize the results obtained from the model with the actual ground truth data. The `show_results` function picks random samples and visualize the ground truth and model predictions, side by side. This enables us to preview the results of the model within the notebook."
    ]
   },
   {