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, dog, banana, car. Many machine learning models today are supervised. Supervised learning allows for classification and regression problems to be solved. We will discuss classification and regression later on in this chapter. 
• Unsupervised learning: It has no label for the training data. Obviously, this type of learning cannot resolve an image of a dog to the label dog. This type of learning model uses mathematical rules to reduce redundancy. A typical use case is to find clusters of like things. 

There also exists a hybrid of both models, called semi-supervised learning, which mixes labeled data and unlabeled data. The goal is to force the machine learning model to organize data as well as make inferences.

The three fundamental uses of machine learning are:

• Classification
• Regression
• Anomaly detection

There are dozens of machine learning and AI constructs that could be talked about with application to IoT, but that would extend far beyond the scope of this book. We will concentrate on a small set of models to understand where they fit in relation to each other, what they target, and what their strengths are. We want to explore the uses and limitations of statistical, probabilistic, deep learning, and RNN, as they are the prevalent areas applicable for IoT artificial intelligence.

Within each of these large segments, we will generalize and dive into the following:

• Random forests: Statistical models (fast model, good for systems with many attributes needed for anomaly detection)
• Bayesian networks: Probabilistic models
• Convolutional Neural Network: Deep learning (deep learning model for unstructured image data)
• RNN: Recurrent neural nets (deep learning model for time series analysis)

Some models are not applicable anymore in the artificial intelligence space, at least for the IoT use cases we consider. So we will not focus on logic-based models, genetic algorithms, or fuzzy logic. 

We will first talk through some initial nomenclature around classifiers and regression.