Abstract: Intelligent systems leveraging artificial intelligence (AI) capabilities frequently depend on machine learning (ML). Machine learning refers to the ability of these systems to learn from specific training data, enabling the automation of analytical model building and the resolution of associated tasks. Unlike traditional programming, where explicit instructions for every possible scenario are coded, machine learning systems identify patterns and make decisions based on data. This capability allows them to improve their performance over time as they are exposed to more data. Machine learning can be divided into several types, including supervised learning, where the system is trained on labelled data; unsupervised learning, which involves finding hidden patterns in unlabelled data; and reinforcement learning, where systems learn by receiving feedback from their actions within an environment. These methodologies empower AI systems to perform a wide range of tasks, from recognizing speech and images to predicting future trends and automating complex processes. The adaptive nature of machine learning makes it a cornerstone of modern AI applications, enabling intelligent systems to handle tasks with a level of efficiency and accuracy that surpasses traditional programming methods. As data continues to grow in volume and complexity, machine learning's role in AI systems becomes increasingly vital.
Keywords: Feature Engineering, Computer Vision, Database, Supervised Learning
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DOI:
10.17148/IJARCCE.2024.13634
