Machine learning can be roughly divided into three categories:
Supervised machine learning
Supervised learning uses data labelled by humans. Supervised data is mainly used to predict events. Supervised learning is best chosen when the desired output of an algorithm is known. In supervised learning, an algorithm learns a series of inputs together with a series of inputs in combination with the corresponding (desired) outputs and the comparison with unwanted outputs. Based on the discrepancy between them, the model adapts.
An example of supervised learning is Apple’s Photos on iOS and Mac OS X. If the user tags a few friends in a number of photos, the software is able to recognise and independently tag these people in photos from now on.
A subcategory of supervised machine learning is classification. Classification is best defined as the attempts to predict an output when the input is known. To do this, the model needs a labelled example in the form of text, speech or an image.
Unsupervised machine learning
For data that is not labelled, unsupervised learning lends itself best. In this variant, the software attempts to discover new patterns in the data without knowing the type of data or labels in any form. This form of machine learning works well for clustering, for example, where data is organised based on similar characteristics.
Reinforcement machine learning
This way of machine learning is strongly based on a theory from psychology. Reinforcement behaviour is nothing but learning by trial and error. This is what a computer is eminently capable of doing. In the case of reinforcement learning, a computer investigates the ideal outcome by simulating it frequently. This form of machine learning is widely used in navigation applications or gaming.