I’d try to answer in the simplest way that I can.
Top-1 accuracy is the conventional accuracy, which means that the model answer (the one with the highest probability) must be exactly the expected answer.
Top-5 accuracy means that any of your model that gives 5 highest probability answers that must match the expected answer.
Let's say you're applying a machine learning algorithm for object recognition using a neural network. A picture of a cat is shown, and these are the outputs of your neural network:
In the above-mentioned probabilities:
Using top-1 accuracy, you will count this output as wrong, because it predicted a tiger.
Using top-5 accuracy, you count this output as correct, because the cat is among the top-5 guesses.
I hope this explanation would help you to understand.
If you want to know the real-world applications of Machine Learning then you can go through this video:
To Learn the application of machine learning, types of machine learning go through this machine learning tutorial.