An Investigation of the Bibliometric Landscape of the Many Facets of Machine Learning From the Years 2000 to 2022

Machine Learning as an emerging and applicable learning plays a vital role in the world of Computer Science. MachineLearning has attracted much attention in the last decade. With the development of Machine Learning, huge researchers and publications opted for this as the path. This paper is about the bibliometric analysis that is deployed from the year 2000 to 2022. The main three fundamental factors that guide the progress of machine learning are Data, computing, and algorithmic advances. Mainly there are four different types of machine learning which accomplish Supervised ML, Unsupervised ML, Semi-Supervised ML, and Reinforcement Machine Learning which is used in different areas and applications. This paper is mainly focused on what are the major areas where machine learning has used and what are the minimal area where there is still a requirement of using Machine learning is to be used.