A Correlative Study of Machine Learning Algorithms

Abstract

Machine learning (ML) is the term used to define a systematic reading of algorithms and mathematical models of computer structure used to perform a specific task without precise programming. Learning algorithms in many programs we use every day. Nowadays, one of the reasons an online search engine like Google performs so effectively is because the learning algorithm has mastered the art of rating web sites. Additional uses for these algorithms include data mining, image processing, predictive analysis, and more, to name a few. The great advantage of using machine learning algorithms is that, when the algorithm finally learns what to do with the data, it can do it on its own. Numerous industries use machine learning, including bioinformatics, intrusion detection, information retrieval, gaming, advertising, malware detection, and more. A general overview of several machine learning algorithms is given in this article. In the beginning, we concentrate on several ML algorithms and highlight the most promising learning approaches in this area. Then, in order to improve accuracy, we look into the close relationships between machine learning techniques and data mining strategies. This paper offers a concise overview and discusses the potential uses of machine learning algorithms in the future.