How AI, machine learning, and deep learning actually nest inside one another, narrow AI versus general AI, the three core learning paradigms, what a model really is, and the AI engineering lifecycle every later module in this program builds on.
"AI" gets used as if it were one single thing, which makes it easy to lose track of what is actually being built. This module untangles the nesting — artificial intelligence as the broad goal, machine learning as the subset that learns from data instead of following hand-written rules, deep learning as the neural-network-specific subset inside that — and draws a hard line between narrow AI (everything actually deployed today) and general AI (a research goal, not a shipped product). It also covers the three core learning paradigms, supervised, unsupervised, and reinforcement learning, at a conceptual level that every later module assumes you already hold.
By the end of this module you should be able to explain what a "model" actually is (a mathematical function with learned parameters, not a black box with intent), walk through the AI engineering lifecycle from data to monitoring, and articulate why AI engineering, the discipline this whole program teaches, is a genuinely different job from AI research. Module 2's machine learning content and Module 13's system architecture both build directly on the foundation this module lays down.