What distinguishes Machine Learning from Deep Learning?

The distinction between Machine Learning (ML) and Deep Learning (DL) is primarily seen in their approach to feature selection. In traditional Machine Learning, feature selection often requires manual intervention, where practitioners need to identify which features or characteristics of the data are relevant and should be used in the model. This process can involve a significant amount of domain knowledge and experimentation.

On the other hand, Deep Learning automates the process of feature extraction and selection through the use of neural networks that automatically learn to identify the most relevant features directly from the raw input data. This capability to learn hierarchical representations of the data allows Deep Learning models to perform exceptionally well, especially with complex data types such as images, audio, and text.

Other statements regarding data requirements, computing power, and the ability to handle unstructured data do not accurately capture the broader distinctions between these two areas in machine learning. For example, while it is true that Deep Learning can require more data to train effectively, it often also necessitates more computing power due to the complexity of the models involved. Therefore, the automation of feature selection distinguishes Deep Learning from traditional Machine Learning, making the chosen answer the most appropriate.