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Abstract
Machine Learning (ML) and Deep Learning (DL) have revolutionized artificial intelligence (AI) by enabling automated decision-making, pattern recognition, and predictive analytics. This paper provides a comprehensive review of classical and modern ML algorithms along with advanced deep learning architectures, highlighting their applications, challenges, and future research directions. The literature review covers the development of these techniques up to 2024, providing insights into the effectiveness of different models in various domains. Additionally, the paper presents comparative analyses through tables and visual representations. Finally, we discuss the impact of computational advancements, challenges in model interpretability, and the ethical implications of AI deployment.
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