Unfolding the Evolution of Machine Learning and its Expediency

The era of computers started on executing complex calculations along with not only storing data, and also storing instructions of manipulating data. It got to a level in which the data processed by computer based on the structural model of real world was expressible on the basis of mathematical terms. Computer's only task was to follow and not learning any new instructions. Next process in the right direction was to make instructions set which would help the computer for experiential learning, for extracting the rules from a huge data which would enable to utilize the rules for classifying and predicting. This led to the start of the era of ML (Machine Learning) along with the field of AI (Artificial Intelligence). There was a big breakthrough in the algorithmic implementations which were modeled on the architecture of brain, and with multi-connecting units that share weighted layers that is organized in DL (Deep Learning). Artificial Intelligence was and is continuing to revolutionize various aspects of our lives with applications in research work especially in biomedical research and healthcare is rapidly improving. In this research paper we try to give a glimpse of past, present and future of Machine Learning from identifying key issues and raise essential research related questions which have to be addressed for maximizing the solutions and gaining more knowledge of different issues regarding Machine Learning.