What is the difference between machine learning and deep learning?

Machine learning (ML) is the discipline covering all models that learn patterns from data instead of following hand-written rules: regressions, decision trees, random forests, gradient boosting, neural networks. Deep learning (DL) is the ML subset specifically based on deep neural networks, meaning ones with many layers: it is the technology behind language models, modern computer vision, and nearly all the generative AI discussed since 2023. The confusion between the two terms is common because everyday language often uses "artificial intelligence" as a stand-in for both, but the relationship is one of inclusion, not equivalence: deep learning uses neural networks with many layers to automatically extract complex patterns from raw data, instead of requiring a domain expert to define those patterns by hand. Understanding this hierarchy matters because choosing between a classic model and a deep learning one is never a question of which is more advanced in the abstract, but of which fits the problem.

See also: Predictive vs Generative vs Agentic AI · Three generations of AI: predictive estimates what will happen, generative produces content, agentic executes tasks. They coexist, they do not replace each other.

Deep learning is the engine of generative AI, not all of ML

Every deep learning system is machine learning, but not every machine learning system is deep learning. Large language models (LLMs) are deep neural networks trained on enormous amounts of text: pure deep learning. But a gradient boosting model predicting customer churn from ten tabular features is equally legitimate machine learning, without a single neural network involved, and often better suited to the task.

When the "classic" model still wins

In the excitement around generative AI it is often forgotten that, on many concrete business problems (demand forecasting, credit risk scoring, fraud detection on structured tabular data), a classic ML model beats deep learning on accuracy, training cost, inference speed and, just as important, explainability: a decision tree can be justified to an auditor or a client in ways a deep neural network often cannot. The right choice is not "the most advanced," but the one proportionate to the problem, the amount of available data and the need (or lack thereof) to explain the decision. Deep learning earns its place when the problem requires capturing complex patterns in unstructured data: text, images, audio.