MACHINE LEARNING: ALGORITHMS, DEVELOPMENT WORKFLOW, APPLICATIONS, LIMITATIONS, AND EMERGING TRENDS

Article Information

Abstract

Machine learning (ML) is the branch of artificial intelligence concerned with algorithms that improve their performance by learning patterns from data. It has become a central method for prediction, classification, recommendation, anomaly detection, language processing, computer vision, and decision support. This paper presents a structured review of the foundations of machine learning, major learning paradigms, commonly used algorithms, model development and evaluation practices, sectoral applications, and continuing challenges. Particular attention is given to data quality, generalization, overfitting, interpretability, fairness, privacy, distribution shift, and the operational difficulties of maintaining models after deployment. The paper also reviews emerging directions including self-supervised learning, federated learning, automated machine learning, foundation models, causal learning, continual learning, and sustainable ML. It argues that successful machine learning depends less on selecting a fashionable algorithm than on defining the problem correctly, obtaining representative data, evaluating the system under realistic conditions, and creating governance and monitoring mechanisms that remain active throughout the model lifecycle.

Keywords

DOI Active Record

This scholarly article has been successfully registered and assigned a persistent DOI through DOIGLOBAL.

Research Tools

Share This Research

Recommended Citation