Artificial intelligence, machine learning and deep learning are nested terms that get used interchangeably. Knowing the actual boundaries helps you understand job descriptions, choose what to learn next and avoid being sold something that does not exist.
The nesting
AI is the broad goal of machines doing tasks that need intelligence. Machine learning is the subset where behaviour is learned from data rather than written as rules. Deep learning is the subset of that which uses multi-layered neural networks. Most of what makes headlines is the innermost circle; most of what makes money is the middle one.
Three learning styles
Supervised learning learns from labelled examples. Unsupervised learning finds structure in unlabelled data. Reinforcement learning learns from rewards over time. Nearly every problem you meet is one of the first two.
The roles
Data analysts explore and explain. Data scientists build and evaluate models. Machine learning engineers put them in production. Research scientists invent new methods. The track leans toward the middle two.
Action Step
Take three real job postings with ‘machine learning’ in the title. For each, classify what is actually being asked for using the roles above and note the skills that repeat.
This course is vendor-independent: it is not affiliated with, endorsed by or accredited by any tool vendor or certification body, names products only for identification, and issues no credential. Verify current documentation before applying anything in production.