chapter 4 (lesson one)

3-transport/lessons/4-geofences/README.md

@@ -14,7 +14,7 @@ This video gives an overview of geofences and how to use them in Azure Maps, top
 
 ## Introduction
 
-In the last 3 lessons, you have used IoT to locate the trucks carrying your produce from your farm to a processing hub. You've captured GPS data, sent it to a cloud storage, and visualized it on a map. The next step in increasing the efficiency of your supply chain is to get an alert when a truck is about to arrive at the processing hub, so that the crew needed to unload can be ready with forklifts and other equipment as soon as the vehicle arrives. This way they can unload quickly, and you are not paying for a truck and driver to wait.
+In the last 3 lessons, you have used IoT to locate the trucks carrying your produce from your farm to a processing hub. You've captured GPS data, sent it to the cloud to store, and visualized it on a map. The next step in increasing the efficiency of your supply chain is to get an alert when a truck is about to arrive at the processing hub, so that the crew needed to unload can be ready with forklifts and other equipment as soon as the vehicle arrives. This way they can unload quickly, and you are not paying for a truck and driver to wait.
 
 In this lesson you will learn about geofences - defined geospatial regions such as an area within a 2km minute drive of a processing hub, and how to test if GPS coordinates are inside or outside a geofence, so you can see if your GPS sensor has arrived or left an area.
 

4-manufacturing/README.md

@@ -10,7 +10,7 @@ As advances happen in Artificial Intelligence (AI) and Machine Learning (ML), th
 
 In these 4 lessons you'll learn how to train image-based AI models to detect fruit quality, how to use these from an IoT device, and how to run these on the edge - that is on an IoT device rather than in the cloud.
 
-> 💁 These lessons will use some cloud resources. If you don't complete all the lessons in this project, make sure you [Clean up your project](../clean-up.md).
+> 💁 These lessons will use some cloud resources. If you don't complete all the lessons in this project, make sure you [clean up your project](../clean-up.md).
 
 ## Topics
 

4-manufacturing/lessons/1-train-fruit-detector/README.md

@@ -60,11 +60,11 @@ Traditional programming is where you take data, apply an algorithm to the data,
 
 ![Traditional development takes input and an algorithm and gives output. Machine learning uses input and output data to train a model, and this model can take new input data to generate new output](../../../images/traditional-vs-ml.png)
 
-Machine learning turns this around - you start with data and known outputs, and the machine learning tools work out the algorithm. You can then take that algorithm, called a *machine learning model*, and input new data and get new output.
+Machine learning turns this around - you start with data and known outputs, and the machine learning algorithm learns from the data. You can then take that trained algorithm, called a *machine learning model* or *model*, and input new data and get new output.
 
-> 🎓 The process of a machine learning tool generating a model is called *training*. The inputs and known outputs are called *training data*.
+> 🎓 The process of a machine learning algorithm learning from the data is called *training*. The inputs and known outputs are called *training data*.
 
-For example, you could give a model millions of pictures of unripe bananas as input training data, with the training output set as `unripe`, and millions of ripe banana pictures as training data with the output set as `ripe`. The ML tools will then generate a model. You then give this model a new picture of a banana and it will predict if the new picture is a ripe or an unripe banana.
+For example, you could give a model millions of pictures of unripe bananas as input training data, with the training output set as `unripe`, and millions of ripe banana pictures as training data with the output set as `ripe`. The ML algorithm will then create a model based off this data. You then give this model a new picture of a banana and it will predict if the new picture is a ripe or an unripe banana.
 
 > 🎓 The results of ML models are called *predictions*
 
@@ -122,6 +122,8 @@ To use Custom Vision, you first need to create two cognitive services resources
     Replace `<location>` with the location you used when creating the Resource Group.
 
     This will create a Custom Vision training resource in your Resource Group. It will be called `fruit-quality-detector-training` and use the `F0` sku, which is the free tier. The `--yes` option means you agree to the terms and conditions of the cognitive services.
+    
+> 💁 Use `S0` sku if you already have a free account using any of the Cognitive Services.
 
 1. Use the following command to create a free Custom Vision prediction resource: