Software automation involves training computer systems with data, called (not surprising) Machine Learning. These techniques are used to make predictions. Different data will use different techniques more efficiently. So let's dive in.
Use Software Engineering Syllabus Support Document to help you understand the depth of knoweldge that NESA requires.Â
All of these worksheet and slide decks introduce the concept of ML and why we use it.Â
Consider this: The most extreme example of ML is GPT and other AI LLMs is based on a Machine Learning technique, in this case a big Neural Network. ML is real, valid and important in modern computing.Â
I did not make this resource. All credit to Ben Jones from Tempe High school. It does, however, give an excellent overview of simple linear regression (to the left) and Multiple Feature Linear Regression (below), including code examples.