Practise one idea at a time. Each activity makes a fresh question, so tap Try another to keep going. Think it through before you check.
AI stores a word's meaning as numbers, a position in space. Words with similar meanings sit close together. Click the plotted word nearest to the query, then Check.
A language model reads lots of text and counts which word tends to follow another. Read the counts, pick the word it is most likely to say next, then Check.
A chatbot says two confident things. One is false. Spot the false one, then pick the counterexample that proves it wrong. Submit when ready.
A spam filter gives each email a confidence score. Anything at or above your threshold gets binned. Slide it and watch the trade-off: catch more spam, but risk binning good mail.
Some tasks have fixed, well-defined logic you can write as a rule. Others are messy patterns that are easier to learn from many examples. Read the task, pick the better approach, then Check.
Building a machine-learning model follows a set order. Tap the 5 lifecycle steps in the order they should happen. Each step you tap drops into Your pipeline. Get all 5 in the right order to pass.
A plain chatbot only writes text (text in, text out). An AI agent runs a Sense, Think, Act loop and calls tools to do things the model alone cannot, like fetching live data or taking an action. Answer each item, then Check.
One connected build. You create a labelled dataset of creatures, then a classifier guesses new creatures by their nearest neighbour. Every prediction depends on the points you placed. Representation, learning, prediction and responsibility, all in one system.