Classical models built from the math you already know
Real data rarely arrives as a clean grid of numbers. A dataset of customers might have a column for city, a column for how someone heard about the product, and a column for household income, and any of those columns can also have gaps where nobody answered. Every model in this course, from a straight line to a decision tree, secretly assumes its input is already a list of numbers with nothing missing. Feature encoding is the work that happens before training even starts, turning categories and blanks into numbers a model can actually use.
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▶ Features & Encoding the Real World