Domain adaptation is the sub-discipline of machine learning and focuses on a well-performed model that learns from source data distribution to a different but related target data distribution. Domain adaptation is applied for training an algorithm from more than one source domain is known as multi-domain adaptation. Domain adaptation is the concept of filling the gap between the source data and target data.

The significance of domain adaptation is to overcome the domain shift issue in numerous real-world applications. The problem in domain shift is that the data distribution represents only from training sets that lead to producing the learning model with less generalization.

Domain or distributional shift is a change in the data distribution, and it is categorized as prior shift, co-variate shift, and concept shift. Domain adaptation with machine learning produces a learning model with high generalization and performance. Domain adaptation in machine learning learns the data from a different domain to a different target domain. Different types of domain adaptation are supervised, unsupervised, and semi-supervised domain adaptation.

Some of the applications of domain adaptations in machine learning: In bioinformatics such as sequence classification and gene expression analysis, and the field of computer vision include the activity recognition of different groups of people, In natural language processing such as sentiment classification, analysis of text from different publications and also used in are of medical image analysis. Recent advancement in domain adaptation is deep domain adaptation and domain adaptation using deep learning.