Key point
The core idea
Machine learning is code that learns a pattern from examples, then uses that pattern on new inputs.
In a project repo, your job is to make this learning process visible: data source, inputs, target, model, test result, and run steps.
Builder translation
Normal code follows rules you write. ML code finds a rule from data you give it.
Example: for a UPI spend tracker, normal code can total expenses. ML could learn from past labelled spends and predict if a new transaction is food, travel, shopping, or bills.