diff --git a/1-getting-started/lessons/1-introduction-to-iot/README.md b/1-getting-started/lessons/1-introduction-to-iot/README.md
index 44335cb8..4af59048 100644
--- a/1-getting-started/lessons/1-introduction-to-iot/README.md
+++ b/1-getting-started/lessons/1-introduction-to-iot/README.md
@@ -135,6 +135,8 @@ Work through the relevant guide to set your device up and complete a 'Hello Worl
 * [Single-board computer - Raspberry Pi](pi.md)
 * [Single-board computer - Virtual device](virtual-device.md)
 
+✅ You will be using VS Code for both Arduino and Single-board computers. If you haven't used this before, read more about it on the [VS Code site](https://code.visualstudio.com?WT.mc_id=academic-17441-jabenn)
+
 ## Applications of IoT
 
 IoT covers a huge range of use cases, across a few broad groups:
diff --git a/4-manufacturing/lessons/1-train-fruit-detector/README.md b/4-manufacturing/lessons/1-train-fruit-detector/README.md
index fd470898..becb263e 100644
--- a/4-manufacturing/lessons/1-train-fruit-detector/README.md
+++ b/4-manufacturing/lessons/1-train-fruit-detector/README.md
@@ -150,6 +150,8 @@ To use Custom Vision, you first need to create two cognitive services resources
 
     ![The settings for the custom vision project with the name set to fruit-quality-detector, no description, the resource set to fruit-quality-detector-training, the project type set to classification, the classification types set to multi class and the domains set to food](../../../images/custom-vision-create-project.png)
 
+✅ Take some time to explore the Custom Vision UI for your image classifier.
+
 ### Task - train your image classifier project
 
 To train an image classifier, you will need multiple pictures of fruit, both good and bad quality to tag as good and bad, such as an ripe and an overripe banana.
diff --git a/5-retail/lessons/1-train-stock-detector/README.md b/5-retail/lessons/1-train-stock-detector/README.md
index 583cf0f1..ada15c31 100644
--- a/5-retail/lessons/1-train-stock-detector/README.md
+++ b/5-retail/lessons/1-train-stock-detector/README.md
@@ -38,17 +38,17 @@ Image classification is about classifying an image as a whole - what are the pro
 
 In the example above, two images are classified using a model trained to classify tubs of cashew nuts or cans of tomato paste. The first image is a tub of cashew nuts, and has two results from the image classifier:
 
-| Tag             | Probability |
-| --------------- | ----------: |
-| `cashew nuts` | 98.4%       |
-| `tomato paste`  | 1.6%        |
+| Tag            | Probability |
+| -------------- | ----------: |
+| `cashew nuts`  | 98.4%       |
+| `tomato paste` | 1.6%        |
 
 The second image is of a can of tomato paste, and the results are:
 
-| Tag             | Probability |
-| --------------- | ----------: |
-| `cashew nuts` | 0.7%        |
-| `tomato paste`  | 99.3%       |
+| Tag            | Probability |
+| -------------- | ----------: |
+| `cashew nuts`  | 0.7%        |
+| `tomato paste` | 99.3%       |
 
 You could use these value with a threshold percentage to predict what was in the image. But what if an image contained multiple cans of tomato paste, or both cashew nuts and tomato paste? The results would probably not give you what you want. This is where object detection comes in.
 
@@ -113,6 +113,8 @@ You can train an object detector using Custom Vision, in a similar way to how yo
 
     > 💁 The products on shelves domain is specifically targeted for detecting stock on store shelves.
 
+✅ Take some time to explore the Custom Vision UI for your object detector.
+
 ### Task - train your object detector
 
 To train your model you will need a set of images containing the objects you want to detect.
@@ -171,12 +173,19 @@ Every time you make a prediction using the quick test option, the image and resu
 
 ## 🚀 Challenge
 
+What would happen if you used the object detector with similar looking items, such as same brand cans of tomato paste and chopped tomatoes?
+
+If you have any similar looking items, test it out by adding images of them to your object detector.
+
 ## Post-lecture quiz
 
 [Post-lecture quiz](https://brave-island-0b7c7f50f.azurestaticapps.net/quiz/38)
 
 ## Review & Self Study
 
+* When you trained your object detector, you would have seen values for *Precision*, *Recall*, and *mAP* that rate the model that was created. Read up on what these values are using [the Evaluate the detector section of the Build an object detector quickstart on the Microsoft docs](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/get-started-build-detector?WT.mc_id=academic-17441-jabenn#evaluate-the-detector)
+* Read more about object detection on the [Object detection page on Wikipedia](https://wikipedia.org/wiki/Object_detection)
+
 ## Assignment
 
 [](assignment.md)