Types of machine learning

Let's try to dissect the different types of machine learning project, starting from the grade of previous knowledge from the point of view of the implementer. The project can be of the following types:

• Supervised learning: In this type of learning, we are given a sample set of real data, accompanied by the result the model should give us after applying it. In statistical terms, we have the outcome of all the training set experiments.
• Unsupervised learning: This type of learning provides only the sample data from the problem domain, but the task of grouping similar data and applying a category has no previous information from which it can be inferred.
• Reinforcement learning: This type of learning doesn't have a labeled sample set and has a different number of participating elements, which include an agent, an environment, and learning an optimum policy or set of steps, maximizing a goal-oriented approach by using rewards or penalties (the result of each attempt).

Take a look at the following diagram: