Machine Learning:
Machine learning provides the technical basis of data mining. It is a branch of artificial intelligence, which concerns the construction and study of systems that can learn from data.
For example, a machine learning system could be trained on email messages to learn to distinguish between spam and non-spam messages. After learning, it can then be used to classify new email messages into spam and non-spam folders.
Types of Machine Learning:
Supervised learning is basically a synonym of classification. The supervision in the learning comes from the labeled instances in the training data.
Unsupervised learning is essentially a synonym of clustering. The learning process is unsupervised since the input instances are not class labeled.
Semi-supervised learning is a class of machine learning technology that make use of both labeled and unlabelled instances when learning a model.
Active learning is a machine learning approach that lets users play an active role in the learning process. An active learning approach can ask a user (e.g., a domain expert) to label an instance, which may be from a set of unlabelled instances.
