TL;DRMachine learning is teaching a model to find patterns from data instead of coding rules by hand.
Instead of writing "if-then" rules, you show a model examples and it learns a function mapping inputs to outputs. Spam detection, recommendations, image labels — all learned from data. The shift is profound: the program is shaped by data, not just by the programmer. Quality and quantity of data become as important as the algorithm.
Key points
Learn the rules from examples, not hand-code them
Model = a learned input→output function
Data quality/quantity is as vital as the algorithm
Powers spam filters, recsys, vision
Common mistakes
Expecting ML without enough/quality data
Treating the model as infallible
Ignoring that data shapes behavior (and bias)
Try it: Contrast solving spam detection with rules vs with ML.
Example code
<!doctype html><html><head><meta charset="utf-8"></head>
<body style="background:#06040d;color:#e6e0ff;font-family:monospace;padding:20px"><pre>rules: if "free $$$" then spam (brittle)
ML: show 100k labeled emails → model learns spam patterns</pre></body></html>