It is easy to mix up artificial intelligence, machine learning, and deep learning because they are closely related and often mentioned in the same sentence. The simplest way to understand them is as nested categories: artificial intelligence is the broad goal, machine learning is one major approach to reaching that goal, and deep learning is a specific technique inside machine learning.
Artificial Intelligence: The Big Umbrella
AI is the overall field concerned with making machines act intelligently. This includes machine learning, but also older approaches such as rule-based expert systems, search algorithms used in chess engines, and symbolic reasoning systems that manipulate logical statements rather than learning from data.
Machine Learning: Learning From Data
Machine learning is a subset of AI where, instead of hand-coding rules, engineers feed a system large amounts of example data and let it discover patterns on its own. A spam filter trained on thousands of labelled emails is a classic machine learning example: nobody wrote a rule for every possible spam phrase, the model learned the patterns itself.
Deep Learning: Learning With Layered Networks
Deep learning is a further subset of machine learning that uses neural networks with many stacked layers, which is where the word 'deep' comes from. These layered networks are particularly good at finding patterns in unstructured data like images, audio, and text, and they are the technology behind modern tools such as image recognition systems and large language models.
So when someone says a product 'uses AI', it could mean anything from a simple decision tree to a massive deep learning model. Knowing the difference helps you ask better questions about how a tool actually works under the hood.