Hierarchy of AI, Machine Learning, and Deep Learning explains how different technologies fit together within the broad field of artificial intelligence (AI). It starts by defining AI as the umbrella concept — machines and systems designed to mimic human cognitive functions such as reasoning, learning, and problem-solving. From there, the article shows how Machine Learning (ML) sits under AI as a specific approach where models learn from data rather than follow fixed rules. Deepening this hierarchy, Deep Learning (DL) is presented as a subset of ML that uses neural networks with many layers to model complex patterns and abstractions from large datasets.
In the article’s schema, AI is likened to the broadest layer — the “parent” concept encompassing any computational technique that enables intelligent behaviour. Within this, Machine Learning is highlighted for its ability to train models using statistical techniques that improve performance as they ingest more examples. This includes popular algorithms like decision trees, clustering, and regression, which can solve tasks from classification to prediction. The author emphasizes that ML’s strength lies in adapting to data patterns rather than relying on handcrafted rules.
Deep Learning is further described as a specialized branch of ML built around artificial neural networks inspired by the human brain. These deep architectures — with many interconnected layers — excel at handling unstructured data such as images, audio, and text. Examples include convolutional neural networks (CNNs) for image processing and recurrent or transformer-based networks for natural language tasks. The article notes that deep learning has driven recent breakthroughs in generative AI, speech recognition, and autonomous systems by uncovering intricate structure within massive datasets.
Finally, the piece stresses that while these technologies are related, they have distinct roles and complexities. AI is the broad goal of intelligent behaviour, machine learning is one effective way to achieve that goal through data-driven training, and deep learning is a powerful but often resource-intensive subset of machine learning. Understanding this hierarchy helps learners and practitioners recognise where different approaches are best applied — from simpler statistical models for structured data to deep neural networks for complex pattern recognition.
