Machine learning is a subset of AI that applies algorithms and statistical models to software (that may help optimize electro-mechanical systems) to automatically improve performance with experience. Machine learning is used to build models and make predictions or decisions without being explicitly programmed to perform the task.
Several types of machine learning include:
Supervised learning: A model is trained on a labeled dataset, where the correct output (label) is provided for a given input. The model can then use this information to make predictions on new, unseen data.
Unsupervised learning: A model is trained on an unlabeled dataset, where the correct output is not provided. The model must find the underlying structure or patterns in the data on its own.
Reinforcement learning: A model learns through trial-and-error interactions with an environment. The model receives feedback in the form of rewards or punishments to guide its learning.
Semi-supervised learning: A model is trained on a partially labeled dataset, where some of the correct output labels are provided, but not all of them.
Machine learning is used in a variety of applications, such as image and speech recognition, natural language processing, and predictive analytics.