Machine Learning
There are three main types of algorithms distinguished by their learning style:
Supervised Learning, Unsupervised Learning, and Semi-Supervised Learning.
Supervised Learning creates an environment where a labeled training set is given and a machine is allowed to make predictions based on the input data. Machines will correct predictions until accuracy reaches a reasonable level, which will allow for generalizations based on the training data. (Ex. Regression, Classification)
Unsupervised Learning gives an unlabeled input set and allows the machine to deduce hidden structures by grouping elements through general rules.(Ex. Clustering, Association rule learning)
Semi-Supervised Learning is a mixture of labelled and unlabelled input data that requires some learning of structures as well as some predictions. (Ex. Regression, Classification)
Supervised Learning, Unsupervised Learning, and Semi-Supervised Learning.
Supervised Learning creates an environment where a labeled training set is given and a machine is allowed to make predictions based on the input data. Machines will correct predictions until accuracy reaches a reasonable level, which will allow for generalizations based on the training data. (Ex. Regression, Classification)
Unsupervised Learning gives an unlabeled input set and allows the machine to deduce hidden structures by grouping elements through general rules.(Ex. Clustering, Association rule learning)
Semi-Supervised Learning is a mixture of labelled and unlabelled input data that requires some learning of structures as well as some predictions. (Ex. Regression, Classification)
Neural Networks
