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Supervised Learning is the machine learning, which is to build a model that makes predictions based on evidence in the presence of uncertainty. As adaptive…

This article talks about Q-Learning, which learns the optimal policy even when actions are selected according to a more exploratory or even random policy. It…

Deep Learning is a term that covers a particular approach to building and training neural networks. It is part of a broader family of machine…

Temporal Difference Learning is an unsupervised technique in which the learning agent learns to predict the expected value of a variable occurring at the end…

This article describe about Reinforcement Learning, which differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected.…

Semi Supervised Learning involves function estimation on labeled and unlabeled data. This approach is motivated by the fact that labeled data is often costly to…

Local Outlier Factor (LOF) is based on a concept of a local density. Local density is estimated by the typical distance at which a point…

This article talks about Ordering Points to Identify the Clustering Structure (OPTICS), which is not a clustering technique per se as it does not output…

Density Based Spatial Clustering of Applications with Noise (DBSCAN) can identify clusters in large spatial data sets by looking at the local density of database…

Fuzzy Clustering is a data clustering technique wherein each data point belongs to a cluster to some degree that is specified by a membership grade.…

Conceptual Clustering is an important and active research area that aims to efficiently cluster and explain the data. It approaches provide descriptions that do not…

Hierarchical Clustering is achieved by use of an appropriate metric, and a linkage criterion which specifies the dissimilarity of sets as a function of the…