Machine Learning is the subfield of AI concerned with algorithms that improve their performance on a task by learning from data. ML encompasses classical statistical approaches (linear regression, decision trees, support vector machines), neural network approaches (deep learning), and reinforcement learning approaches where systems learn from interaction with an environment.
The modern AI boom is built on a specific subset of ML — deep learning with transformer-based neural networks — but the broader ML toolkit remains in widespread production use. Credit scoring, fraud detection, recommendation systems, and computer vision pipelines still rely heavily on classical ML techniques where the training data is structured, the interpretability matters, and the cost of inference on large neural networks is not justified.
Distinguishing "ML" from "AI" is more about emphasis than strict taxonomy. In CrashBytes coverage, "ML" typically refers to the engineering discipline of training and deploying learned models, while "AI" refers to the broader category of intelligent systems, of which ML is the dominant technical approach as of 2026.