Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. The process of learning begins with the observations or data/information, such as examples, direct experience, or we can set instruction. We will keep you posted on the latest Expert System products, their solutions and with ML services, cognitive computing and share the most interesting information on semantics and Artificial Intelligence from around the web, and from our rich library of white papers, customer case studies and more.
Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. In contrast, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards.