What distinguishes machine learning (ML) from deep learning (DL)?

Machine learning (ML) and deep learning (DL) are both subfields of artificial intelligence, but they differ significantly in their approaches to data processing and feature extraction. The statement that ML utilizes human-labeled features while DL identifies its own patterns is accurate in delineating these differences.

In machine learning, many models require explicit feature engineering, where humans select and define the features that will be used for training the model. This process involves domain knowledge to determine which aspects of the data may be relevant for making predictions. Traditional algorithms can utilize these labeled features to make decisions based on predefined patterns.

In contrast, deep learning, which typically involves neural networks with multiple layers, can automatically discover and extract relevant features from raw data without needing manual intervention. This capability allows deep learning systems to process unstructured data such as images, audio, or text, identifying intricate patterns and representations that may not be easily recognizable to humans.

Thus, the key distinction lies in the feature extraction process: machine learning relies on features that are defined by human experts, whereas deep learning systems can autonomously learn and identify features directly from the data input itself.