Abstract
Machine learning (ML) has become a significant interdisciplinary field within artificial intelligence (AI) due to the rapid growth of digital data and the increasing need for data-driven analysis, prediction, and decision support in various domains. The effective application of ML is contingent on problem formulation, data quality, algorithm selection, evaluation, interpretability, computational requirements, security, and deployment conditions. This study aims to provide an analytical overview of ML by integrating four interconnected dimensions: the ML process, major learning algorithms, application domains, and emerging challenges. In particular, it examines the relationship between problem properties, data properties, algorithm selection, model performance, and practical application. An analytical narrative literature review combined with document analysis was adopted. Foundational books, peer-reviewed studies, and selected authoritative technical sources were reviewed and analysed to identify the main learning paradigms, algorithm selection criteria, application patterns, and implementation challenges. According to the review, ML should be viewed as a multi-stage process encompassing problem definition, data acquisition, data preparation, model development, evaluation, deployment, and monitoring. Supervised, unsupervised, and reinforcement learning constitute the principal learning paradigms, while ensemble learning represents a model-combination strategy. Algorithm selection is influenced by task characteristics, data structure and quality, dataset size, computational requirements, evaluation criteria, and interpretability needs. Major challenges include limited or poor-quality data, overfitting, generalization, computational demands, privacy, security, interpretability, and deployment conditions. The study emphasizes that algorithm suitability and ML performance should be assessed within the specific context of the problem, data, and operational requirements.
Academians Publishers