AI isn't magic. It reads words by chopping them into pieces, it "sees" pictures as grids of numbers, and it learns in three ways — with a teacher (supervised), all by itself (unsupervised), or by trial & reward (reinforcement). Pick your class to play through every chapter of your handbook — or explore the playgrounds by topic below.
All 4 chapters — AI domains, AI in industries, data visualisation, and ethics & bias — as hands-on playgrounds with a quiz on every page.
Open Class 7 → 88️⃣All 4 chapters — the AI project lifecycle, deeper applications, data & fairness, and responsible AI — as hands-on playgrounds with a quiz on every page.
Open Class 8 →Chop a sentence into pieces and give each a number.
Play → 2🧹Stop-words, stemming & lemmatization — words to their roots.
Play → 3🔢Turn words into numbers so similar words sit close together.
Play → 4🔮The probability game every language model plays.
Play → ★🤖The big one. Watch the model learn from text, then write one token at a time.
Open the big lab →Learn from labelled examples, draw a boundary, then sort new things. (Needs a teacher.)
Play → 7🪄No labels at all — watch the computer find the groups by itself.
Play → 📈📈Fit a trend line and predict a number — like next test marks. (The partner of classification.)
Play → 🕹️🕹️A robot learns a path to the 🏆 by trial & reward — no labels, no teacher. Train it and watch it get smarter. (The "RL" behind game-playing AIs and chatbots.)
Play →Split data into training / validation / test and sort the data types.
Play → 📊📊Turn numbers into bar / line / pie charts, then play the precision-vs-accuracy game.
Play →Explore 6 real-world sectors, then play match-the-application.
Play → 🔄🔄Walk the 6 stages, sort Automation vs AI, run a score predictor.
Play → ⚖️⚖️Unbalance the training data and watch the AI turn unfair.
Play → 🛡️🛡️App permissions, misinformation, and who's accountable.
Play →Unzip and double-click index.html — runs with no internet.