AN ANALYSIS OF MACHINE LEARNING TECHNOLOGY AND USES IN THE MODERN INDUSTRIES

Abstract

The main purpose of this research is to explore the different machine learning methods and their applications. Machine learning is a technology that has immense potential across different sectors, and has been the center of attention in recent years. This empirical study endeavors to dissect and examine the practical applications of machine learning techniques, focusing on their effectiveness, hurdles, and potential influence. We also provide an outline of key algorithms used in machine learning and their practical implications. This study seeks to provide insights into the potentialities and restraints intrinsic to these technologies while also promoting sensible AI evolution. Ultimately, its aim is twofold: enabling knowledgeable decision-making alongside encouraging the conscientious and accountable integration of machine learning methodologies thereby nurturing progressions and constructive transformations through industries. I will delve deeper into these sophisticated techniques highlighting their capabilities and restrictions. For instance, in the healthcare sector, we discuss how ML has been employed for disease diagnosis drug discovery endeavors, and personalized treatment plans. In the finance domain, we explore its utilization in fraud detection algorithmic trading strategies and risk estimation procedures.