Beginner course · Free
AI Foundations
Understand what AI is, how systems learn, where they fail, and how to judge claims without hype.
For: Anyone who wants a reliable mental model of modern AI
- What AI is2 guides
- How systems learn3 guides
- Models and inference3 guides
- Evidence and uncertainty3 guides
- 4Modules
- 11Guides
- ~4hTo complete
- 6Competencies
Outcomes
What you will be able to do
- Explain training and inference in plain language.
- Separate demonstrated capability from marketing claims.
- Evaluate AI outputs using evidence, uncertainty, and fit-for-purpose tests.
Prerequisites: None.
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Curriculum
Course modules
- Module 1
What AI is
Outcome: Distinguish AI, machine learning, and ordinary software.
Practice: Classify five everyday systems and explain which definition of AI you used.
AI concepts and boundaries - Module 2
How systems learn
Outcome: Describe the role of examples, objectives, and generalization.
Practice: Diagnose why a model can perform well in testing but fail for a new population.
Data, training, and generalization - Module 3
Models and inference
Outcome: Explain what happens when a trained model produces an output.
Practice: Trace one user request from input through model output and human review.
Models, inference, and limitations - Module 4
Evidence and uncertainty
Outcome: Check claims, benchmarks, and confident-sounding outputs.
Practice: Create a claim ledger for one AI product announcement.
Evaluation and benchmark literacyUncertainty and confidence calibrationSource and claim verification
Applied capstone
AI claim fact check
Choose one public AI claim and produce a one-page evidence review for a nontechnical reader.
Evidence checklist
- Original claim and source
- Two supporting or contradicting sources
- Known limitations
- Plain-language conclusion with confidence