Machine learning involves computers recognizing patterns and learning rules. Instead of just being able to react to the input of human users, machines should be able to make decisions independently based on the rules they have learned. For example, algorithms can learn to correctly identify spam or to understand the content of an image. Developers and scientists use a variety of methods for training. The most commonly used method is probably supervised learning.
In supervised machine learning, developers provide the algorithms with a prepared data set as a training source. In other words, the results are provided upfront. The algorithm’s job is to recognize the pattern: why does this data belong to category A and not to category B?
Supervised learning is used for algorithms that are supposed to categorize types of data (e.g. photos, handwriting, language, etc.). Another common field of application for supervised learning is regression problems. In this case, the algorithms are supposed to make predictions, such as price trends or increased sales.
Semi-supervised learning is a hybrid form. When using this learning method, only part of the data set is labeled. The rest remains uncategorized and should be assigned to categories by the algorithms independently. An example of this is Facebook’s facial recognition function. All you need to do is tag a few photos with the names of friends. The algorithm will find the rest on its own.