Now that we know what machine learning involves-learning a set of rules (or building a model) by looking at examples and then using these rules to work out answers for previously unseen data-let's dig a little deeper. In this section, we will discuss two major categories of learning algorithms-supervised and unsupervised learning. These two categories differ in the nature and type of data being presented to the learning algorithm.
Instead of working with formal definitions, let's go with examples. Let's say that we are interested in building a machine learning system that can differentiate between the images of cats and dogs. That is, given an image, our algorithm should tell us whether the picture is that of a cat or a dog. Following the general guidelines that we laid out in the previous section, we have to present our system with a set of example images from where the learning will take place. For such a problem, we present to our system, what are known...
