Understanding AI
Most explanations of AI are either marketing or mathematics. This course sits between the two: what these systems actually do, why they work, where they fail, and how to use them without being misled.
What you'll learn
12 chapters, then the quiz
- 01What AI meansA moving label, not one technology.
- 02How machine learning worksGuess, measure the error, adjust.
- 03Training dataThe model is what it read.
- 04Kinds of learningSupervised, unsupervised, reinforcement.
- 05Neural networksLayers of weighted sums.
- 06Large language modelsPredicting the next piece of text.
- 07Limits and hallucinationWhere these systems go wrong.
- 08Bias and fairnessPatterns in the data become patterns in the output.
- 09Using AI wellGetting value without getting caught out.
- 10Privacy and safetyWhat happens to what you type.
- 11Beyond textImages, audio and agents.
- 12AI and workA sober view of the change.
More ai & data tests
See all →- Medium
Data analysis
Averages, spread, correlation and the traps that make numbers say the wrong thing.
8 questions7 min - Medium
Machine learning basics
Training, testing and the workflow behind a model that actually works.
8 questions8 min - Hard
Neural networks
Layers, weights and backpropagation, explained without the linear algebra.
8 questions8 min