The comparison between Deep Learning vs Machine Learning highlights key distinctions in capability and complexity. Machine Learning (ML), a subset of AI, requires manual feature engineering, works with smaller datasets, and runs on moderate hardware. In contrast, Deep Learning (DL) is a specialized subset of ML that uses deep neural networks to automatically extract features, process vast amounts of unstructured data, and perform complex tasks like image and speech recognition. While ML excels in structured, interpretable scenarios, DL thrives in handling unstructured data with minimal human intervention, though it demands significant computational resources. Choosing between them depends on data scale, task complexity, and hardware availability.

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