What is Machine Learning, and how does it differ from traditional programming?

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Machine Learning is a paradigm where computers learn from data to make predictions or decisions, rather than being explicitly programmed with rules. Traditional Programming : Input + Rules → Output . The developer writes explicit logic. Machine Learning : Input + Output → Rules (Model) . You give the system examples (data) and target outputs, and it figures out the rules. Why ML matters: for problems too complex to hand-code (image recognition, language translation, fraud detection), ML scales by learning patterns directly from data. Common ML tasks include classification (spam/not spam), regression (predict house price), clustering, and recommendation. Frame the difference around 'who writes the rules.' In traditional programming, the developer codes the rules; in ML, the model learns the rules from data. Use a fraud detection example — writing every fraud rule by hand is impossible, but a model can learn patterns from millions of transactions.