Final answer:
Machine learning is like a toy that learns from playing games or seeing pictures, getting better over time. It's similar to how kids learn to recognize animals like dogs or how they get better at riding bicycles by practicing and learning from mistakes.
Step-by-step explanation:
Think of machine learning like this, imagine you have a toy that can learn. At first, it doesn't know how to play any games. But, every time you play with it, the toy learns a little bit more. If you show it a picture of a cat, the next time it sees something similar, it guesses, 'Oh! That's a cat too!' Just like when you learned what a dog is by seeing your family's Labrador retriever and then knowing other dogs in books are also 'dogs.' Machine learning works in a similar way, but with computers making the guesses after being shown lots of examples.
Just as you learn to ride a bicycle by practicing and falling over a few times before you get it right, a machine learns by trying and making mistakes too. Eventually, the machine gets better and better, just like you get steadier and faster on your bike.