Separate a rule you hand-write from a pattern a model learns off graded examples, then tag the feature columns, target, and prediction in a real email table.

You could write `if subject contains "FREE" then spam` and ship it, but the moment a spammer types "Fr€€" your rule is dead. There is a second way to build that filter, and it never touches a keyword list.

Sort any problem into supervised, unsupervised, or reinforcement by the shape of its data, a filled answer column, no answers, or a reward earned by acting, never by its subject or algorithm.

Last topic you graded emails `spam` or `not-spam` and trained a filter, and that answer column is the whole reason it worked. Delete the column and you can still learn something from those emails, just not the same thing.

Trace one pass of the training loop, guess, measure the miss, nudge the weight, then predict what a too-big or too-small step does to the loss curve and when more passes stop paying off, all with zero math.

Last topic your filter could call an email `spam`, but nobody told it how. It starts out guessing, gets one number back for how far off it was, and quietly tunes itself, over and over, until the guesses stop missing.

Run any problem through a four-condition test to tell a hand-written rule from a trained model, then judge whether your data is enough, labeled, and representative before writing a line of code.

You could write `if income < 12000: tax = 0` in one line, and you should. Other problems bury their answer in examples no line you type will ever catch, and telling the two apart comes first.