Machine learning includes the following main types:
The main difference between the two types is that supervised learning is performed using a basic truth. The operator, therefore, has prior knowledge of what the output values of our samples should be (e.g. right or wrong). Therefore, supervised learning is aimed at learning a function which, given a sample of desired data and outputs, is closer to the relationship between inputs and outputs which can be observed in the data.
Unsupervised learning has no labeled outputs. Therefore its goal is to deduce the structure within a group of data.
Supervised learning issues can be further grouped into regression and classifier issues.
Classifier: there is a classifier problem when the output variable is a category, like “red” and “blue” or “right” and “wrong”
Regression: a regression problem is when the output variable is a real value, like “dollars” for the estimate of a house, whose input parameters are “size”, “nearby schools”, etc.
The complexity of the model refers to the complexity of the function one is trying to learn.
Unsupervised learning is when the operator enters the inputs (such as images) with no matching output variable. The goal of unsupervised learning is to model the structure or the distribution underlying the data. Unlike supervised learning, there are no correct answers because there is no teacher. The algorithms discover the structures in the data and the laws governing them on their own.
Unsupervised learning issues are essentially clustering problems since one wishes to discover the inherent groupings in the data, for example, clustering objects according to their area. The dimensionality reduction of the problem is an aspect which cannot be overlooked, as significant features must be chosen (for example selecting objects according to the area might not be enough)