In machine learning, the classification problems deal with discrete targets with a finite set of possible values. What this means is that there is a set of possible outcomes, and given some features we want to predict the outcome.
The binary classification is the most common type of classification problem, as the target variable can have only two possible values, such as True/False, Relevant/Not Relevant, Duplicate/Not Duplicate, Cat/Dog, and so on.
Sometimes the target variable can have more than two outcomes, for example, colors, category of an item, model of a car, and so on, and we call this multi-class classification. Typically, each observation can only have one label, but in some settings an observation can be assigned several values. Multi-class classification can be converted to a set of binary classification problems, which is why we will mostly concentrate on binary classification.