MACHINE LEARNING METHODOLOGIES

Abstract

Machine Learning (ML) methodologies have revolutionized various domains by
enabling computers to learn patterns from data and make predictions or decisions. This paper
provides an overview of key ML methodologies, including supervised learning, unsupervised
learning, and reinforcement learning. Supervised learning involves training models on labeled data
to predict outcomes, while unsupervised learning focuses on discovering hidden patterns in
unlabeled data. Reinforcement learning, inspired by behavioral psychology, revolves around
learning optimal decision-making through trial and error. Additionally, this paper discusses
ensemble methods, deep learning, and their applications across diverse fields such as healthcare,
finance, and autonomous systems. Understanding these methodologies and their applications is
crucial for practitioners and researchers to leverage the power of ML in solving real-world
problems effectively.