Abstract
Machine learning (ML) has become a revolutionary technology with diverse applications across fields such as healthcare, finance, and autonomous systems. This paper presents a comprehensive overview of recent developments in ML techniques, algorithms, and their applications. It discusses key concepts such as Supervised Learning, unsupervised Learning, reinforcement learning, and deep learning, highlighting their strengths, weaknesses, and real-world applications. Additionally, the paper explores cutting-edge research in areas such as transfer learning, generative adversarial networks (GANs), federated learning, and explainable AI. Furthermore, it examines challenges and future directions in the field, including ethical considerations, robustness, interpretability, and scalability. This review seeks to equip researchers, practitioners, and enthusiasts with a comprehensive understanding of the current landscape of machine learning and its potential societal impact.
Keywords: Adversarial networks, AI, Deep learning, Explainable ethical considerations, Generative federated learning, Machine Learning (ML), Supervised learning, Unsupervised learning.
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