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Rinki Singh

Deepti Goyal

Ritu Punhani

Nidhi Sharma

Anju Rani

Shalini Puri

Abstract

This paper offers a comprehensive overview of machine learning, covering its theoretical foundations, practical applications, and future research direc-tions. It explores the fundamental concepts, historical context, and mathe-matical underpinnings of machine learning, including linear algebra, calcu-lus, probability theory, and optimization The study grapples with crucial is-sues facing the discipline, including the reliability of datasets, the transparen-cy of models, and the moral implications of the research. It outlines a general framework for developing machine learning models and examines emerging research areas like causal AI, federated learning, and energy-efficient algo-rithms. This work highlights the field's ongoing evolution and its potential to address complex real-world problems.

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