Machine learning, as a sub-area of artificial intelligence, enables computers to learn independently from data and experience without explicit programming. The focus is on autonomous learning, whereby the algorithm generates its code automatically. The training data, presented as features or images, is used to gradually improve performance.
Different machine learning models use various algorithmic techniques and can be divided into supervised, unsupervised, partially supervised or reinforcement models. These models can handle tasks such as predictions, probability calculations, group recognition, sequence analysis, dimension reduction and process optimization.
Artificial intelligence forms the overarching element, which includes machine learning, deep learning and neural networks. Within machine learning, algorithms are designed to make classifications, pattern recognition, predictions and informed decisions. These algorithms can be used individually or in combination to achieve the highest accuracy with complex and unpredictable data.