Internet of Things for Architects

Machine learning models

We will now focus on specific machine learning models that have applicability to IoT. There is no single winner of the model one will use to sift through a collection of data. Each model has its particular strength and the use case it serves. The goal of any machine learning tool is to arrive at a prediction or inference of what a set of data is telling you. You want to be better than flipping a coin of 50%. 

There are two types of learning systems to consider which are as follows:

• Supervised learning: It simply implies that the training data provided to the model has an associated label with each entry. For example, a set may be a collection of pictures each labeled with the content of that image: for example, cat, ...