spelling: the

Signed-off-by: Josh Soref <2119212+jsoref@users.noreply.github.com>

1-getting-started/lessons/1-introduction-to-iot/virtual-device.md

@@ -159,7 +159,7 @@ Create a Python application to print `"Hello World"` to the console.
 
     If you don't have `.venv` as a prefix on the prompt, the virtual environment is not active in the terminal.
 
-1. Launch a new VS Code Terminal by selecting *Terminal -> New Terminal, or pressing `` CTRL+` ``. The new terminal will load the virtual environment, and the the call to activate this will appear in the terminal. The prompt will also have the name of the virtual environment (`.venv`):
+1. Launch a new VS Code Terminal by selecting *Terminal -> New Terminal, or pressing `` CTRL+` ``. The new terminal will load the virtual environment, and the call to activate this will appear in the terminal. The prompt will also have the name of the virtual environment (`.venv`):
 
     ```output
     ➜  nightlight source .venv/bin/activate

5-retail/lessons/1-train-stock-detector/README.md

@@ -62,7 +62,7 @@ When you then use it to predict images, instead of getting back a list of tags a
 
 ![Object detection of cashew nuts and tomato paste](../../../images/object-detector-cashews-tomato.png)
 
-The image above contains both a tub of cashew nuts and three cans of tomato paste. The object detector detected the cashew nuts, returning the bounding box that contains the cashew nuts with the percentage chance that that bounding box contains the object, in this case 97.6%. The object detector has also detected three cans of tomato paste, and provides three separate bounding boxes, one for each detected can, and each one has a percentage probability that the bounding box contains a can of tomato paste.
+The image above contains both a tub of cashew nuts and three cans of tomato paste. The object detector detected the cashew nuts, returning the bounding box that contains the cashew nuts with the percentage chance that the bounding box contains the object, in this case 97.6%. The object detector has also detected three cans of tomato paste, and provides three separate bounding boxes, one for each detected can, and each one has a percentage probability that the bounding box contains a can of tomato paste.
 
 ✅ Think of some different scenarios you might want to use image-based AI models for. Which ones would need classification, and which would need object detection?