Machine Learning, or ML, is like teaching a computer how to learn from experience, just like how you learn new things over time.
Think about how you learned to ride a bike. At first, you might have fallen a few times, but with practice, you got better because you learned from your mistakes. Machine Learning works in a similar way, but instead of you learning, it’s a computer that’s learning.
Imagine you have a robot that needs to recognize pictures of cats and dogs. Instead of programming it with all the specific details of what makes a cat or a dog, you show it thousands of pictures of both. The robot looks at these pictures and starts to notice patterns—like cats often have pointy ears and dogs usually have different-shaped snouts. Over time, the robot gets better at telling the difference between cats and dogs because it has learned from all the examples you showed it.
In simple terms, Machine Learning is about giving a computer lots of examples and letting it learn patterns from those examples, so it can make decisions or predictions on new information.
