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. ‎
KEYWORDS: Algorithm, Machine Learning, Supervised learning, unsupervised learning, ‎