Discovering the different types of machine learning

The power of machine learning is due to the quality of its algorithms, which have been improved and updated over the years; these are divided into several main types depending on the nature of the signal used for learning or the type of feedback adopted by the system.

They are as follows:

• Supervised learning: The algorithm generates a function that links input values to a desired output through the observation of a set of examples in which each data input has its relative output data, which is used to construct predictive models.
• Unsupervised learning: The algorithm tries to derive knowledge from a general input without the help of a set of pre-classified examples that are used to build descriptive models. A typical example of the application of these algorithms is found in search engines.
• Reinforcement learning: The algorithm is able to learn depending on the changes that occur in the environment in which it is performed. In fact, since every action has some effect on the environment concerned, the algorithm is driven by the same feedback environment. Some of these algorithms are used in speech or text recognition.

The subdivision that we have just proposed does not prohibit the use of hybrid approaches between some or all of these different areas, which have often recorded good results.