Machine learning as the word defines itself, we have a machine who wants to learn, and just like 1
humans, machines also have a different method for learning something. 2
We have supervised learning. 3
They have unsupervised learning. 4
And finally, we have reinforcement learning. In this Supervised learning. 5
We have different techniques, like regression and we also have classifications just like image classifications, 6
If you have any image processing field and you need to have somehow classifications of your data. 7
then Supervised learning going to be the best for unsupervised learning. 8
We have like a big data validation. 9
Whenever you are dealing with some big data and you want to label them, that's the best method to use 10
here. 11
We have targeted marketing, customer segmentation, and more. For reinforcement learning. 12
It deals with robot navigation and mapping, skill acquisition. 13
When we have some system or a kind of robot that wants to learn a new skill, then this is the learning 14
method that we need to use. 15
We have a game A.I. 16
We have a real time decision making and finally the learning tasks. 17
So let's get to know each of them better with some examples, of course.