An Assessment of Machine Learning and Deep Learning Techniques with Applications

Abstract

Nowadays, sheer amount of data is being generated by plethora of sources, like science, business, medicine, sports, geography, environment, etc. This produced data may be formless, bigger sized, and in the raw format and has no importance at all, until analyzed. Conventional techniques of data analysis may be inappropriate due to vast data diverse nature, high dimensionality of data, and much of the data is never explored. So, in order to get relevant data, some techniques need to be incorporated on the existing data, which would be effective for the real-world applications. Artificial intelligence, machine learning, and deep learning are the extensively used technologies with the utmost buzz. Machine learning is a subfield of AI that designs the intelligent model based on past and current trends. The only concentration of this ground is pre-programmed learning techniques without any human interference/intervention. In addition to this, deep learning processes the data and creates pattern for decision use after imitating the working of human brain. So, the objective of this paper is to explore the research application areas and the widely used approaches/techniques in the domain of machine learning and deep learning.