The primary advantage of deep learning over conventional machine learning is its ability to perform automatic feature extraction. In conventional machine learning, human experts must manually analyze raw data to engineer and select valuable features. In contrast, deep neural networks utilize their multi-layered architecture to automatically learn hierarchical representations: early layers extract basic, specific features, while deeper layers build upon them to learn increasingly abstract concepts, allowing the model to be trained directly on unstructured data.