PART 2 · MODULE 1The basics
What is Machine Learning?
The basics of machine learning
Loading module...
The basics of machine learning
Traditional software follows rules that a programmer writes: "if the temperature is above 90, turn on the AC." Machine learning flips this around. Instead of writing rules, you show the computer thousands of examples and let it figure out the rules on its own.
Think of it like teaching a child to recognize cats. You don't hand them a checklist of "four legs, whiskers, pointy ears." You show them hundreds of cat pictures, and eventually they just "get it." That's machine learning in a nutshell.
Regular programming: you write the recipe. Machine learning: you show the computer thousands of cakes and it figures out the recipe itself.
Learning from labeled examples, like a teacher grading homework. You show the model inputs and correct answers.
e.g. Spam filters, price predictions
Finding hidden patterns without labels. The model discovers structure in data on its own.
e.g. Customer grouping, anomaly detection
Learning by trial and error with rewards. Like training a dog with treats for good behavior.
e.g. Game-playing AI, robotics
Every ML project follows a similar path: gather data, train a model on that data, and then use the model to make predictions on new, unseen information.
THE MACHINE LEARNING PIPELINE
Machine learning finds patterns in data that humans might miss. A model can analyze millions of data points and spot subtle connections that would take a person years to notice. That's its superpower.