The Significance of Machine Learning and its Applicability in the Research Field.

The last decade has seen a significant number of remarkable expansions in machine learning research. The field has achieved unprecedented popularity by developing new areas and increased momentum on the existing sites. Whereas the symbolic methods have been exceedingly active, the sub-symbolic approaches, especially the neural networks, have had tremendous growth. Unlike the efforts whose main concentrations were on the single learning strategies, the new trends have been to integrate various methods and for the development of cognitive learning architectures. Besides, there is an increased interest in the experimental comparisons of several methods and learning algorithms' theoretical analysis. The same data sets have often been shared amongst the researchers who have led to applying the same techniques in dealing with the same problems to understand the relative merits of various methods. There are new insights that have been brought into the complexity of the learning process through theoretical investigations. This research paper seeks to transmit the knowledge of machine learning, which has become very popular and the researchers' applications of the experience. [ABSTRACT FROM AUTHOR]