Supervised learning is a machine learning technique that uses labeled data sets to train artificial intelligence algorithms models to identify the underlying patterns and relationships between input features and outputs. The goal of the learning process is to create a model that can predict correct outputs on new real-world data.| www.ibm.com
Neural networks allow programs to recognize patterns and solve common problems in artificial intelligence, machine learning and deep learning.| www.ibm.com
Artificial superintelligence is a hypothetical software-based AI system with intellect beyond human intelligence.| www.ibm.com
In reinforcement learning, an agent learns to make decisions by interacting with an environment. It is used in robotics and other decision-making settings.| www.ibm.com
This privacy statement describes how IBM collects, uses, and shares personal information about consumers and other individuals within our clients, business partners, supplier and other organizations with which IBM has or contemplates a business relationship.| www.ibm.com
Large language models are AI systems capable of understanding and generating human language by processing vast amounts of text data.| www.ibm.com
Natural language processing (NLP) is a subfield of artificial intelligence (AI) that uses machine learning to help computers communicate with human language.| www.ibm.com
Unsupervised learning, also known as unsupervised machine learning, uses machine learning (ML) algorithms to analyze and cluster unlabeled data sets.| www.ibm.com
Logistic regression estimates the probability of an event occurring, such as voted or didn’t vote, based on a given data set of independent variables.| www.ibm.com