Fundamentals of AI and ML – AIF-C01 Study Guide
This domain accounts for 20% of the exam and establishes the vocabulary the rest of the certification builds on: what AI, ML, and deep learning are; the learning paradigms; how inference is served; data types; and the ML lifecycle. Questions are conceptual and scenario-based — no math. Master the definitions and the "when is ML the right tool?" judgment and this domain scores reliably.
AI, ML, and Deep Learning
Think of three nested circles:
- Artificial intelligence (AI) — the broad field of building systems that perform tasks requiring human-like intelligence.
- Machine learning (ML) — a subset of AI where systems learn patterns from data rather than being explicitly programmed.
- Deep learning — a subset of ML using multi-layered neural networks, excelling on unstructured data (images, audio, text).
Containment: deep learning ⊂ machine learning ⊂ AI.
Learning Paradigms
| Paradigm | Learns from | Example |
|---|---|---|
| Supervised | Labeled examples | Spam/not-spam, price prediction |
| Unsupervised | Unlabeled data (finds structure) | Customer segmentation (clustering) |
| Reinforcement | Rewards/penalties via trial and error | A robot learning to walk |
The discriminator: labels present → supervised; no labels, find groups → unsupervised; rewards from interaction → reinforcement.
Within supervised learning: classification predicts a category, regression predicts a continuous number.
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5 more sections (about 310 words) continue below. The first domain's study guide is free for every AIF-C01 learner with a free account.