Edits suggested for this course (#28)

modules/ROOT/pages/index.adoc

@@ -36,7 +36,7 @@ You are expected to complete the _Red Hat OpenShift AI Administration_ course, w
 == Prerequisites
 
 * Take the Introduction to https://www.redhat.com/en/services/training/ad141-red-hat-training-presents-introduction-to-python-programming[Python Programming (AD141)] training course or demonstrate equivalent knowledge of Python programming
-* Basic knowledge of machine learning foundations is recommended.
+* Basic knowledge of machine learning foundations is recommended
 * Basics of creating and consuming REST APIs programmatically 
 * Basic knowledge of the Python libraries for machine learning and deep learning
 * Experience with OpenShift is recommended, but not mandatory (in case students have to do troubleshooting)

modules/chapter1/pages/section2.adoc

@@ -243,7 +243,7 @@ Deploying a **Model Server** triggers a **ReplicaSet** with **ModelMesh**, which
 
 == Test The Model
 
-Now that the model is ready to use, we can make an inference using the REST API
+Now that the model is ready to use, we can make an inference using the REST API.
 
 . Assign the route to an environment variable in your local machine, so that we can use it in our curl commands.
 +

modules/chapter1/pages/section3.adoc

@@ -4,7 +4,7 @@ A model-serving runtime provides integration with a specified model server and t
 
 As an administrator, you can use the OpenShift AI interface to add and enable custom model-serving runtimes. You can then choose from your enabled runtimes when you create a new model server.
 
-== Prerequisite
+== Prerequisites
 
 In order to run this exercise, be sure to have handy the model we created in the previous section, that is: