What is the difference between machine learning and deep learning?

Machine learning is a field of AI focused on building systems that learn from data, identify patterns, and make decisions with minimal human intervention rather than being explicitly programmed for every task. Deep learning is a subset of machine learning that uses multi-layered artificial neural networks inspired by the human brain, making it well suited for large, complex, nonlinear data such as images, audio, and text. In short, deep learning is a specialized branch of machine learning, and neural networks are the building blocks used in deep learning.

According to the source, machine learning (ML) builds systems that learn from training data: instead of coding rules like "if the image has pointy ears and whiskers, it is a cat," an ML algorithm is fed many labeled examples, finds patterns in features such as shapes, colors, and textures, and improves with more data. Deep learning (DL) is a subset of ML that uses algorithms inspired by the human brain, called artificial neural networks. These networks have multiple layers of interconnected neurons, and the word "deep" refers to those many layers. DL is especially good at modeling complex, nonlinear relationships in large datasets, for example recognizing faces in photos, understanding voice commands, translating languages, or helping self-driving cars detect objects. The source summarizes the relationship clearly: ML is about learning from data, DL is a specialized branch of ML that uses multi-layered neural networks, and neural networks are the building blocks that allow DL to model complex relationships.