Next topic would be unsupervised learning. 1
This is where those labels are omitted, rather, unlabeled 2
It is form of machine learning rather than being trained with sample data, 3
the machine learning system finds structures and patterns in the data on its own. With unsupervised 4
algorithms, 5
you still don't know what you want to get out of the model yet! 6
You have a bunch of data, but you really don't know how to categorize them, how to put them in different 7
groups and you want the machine to do it for you in this case. 8
So you normalize your data into a format that makes sense to compare them and then let the model work 9
its magic and try to find some of these relationships. 10
One of the special characteristic of this model is that while the model can suggest different ways to 11
categorize or order your data, it's also at the end up to you to make the furthermore research on those 12
datasets and make a decision. 13
For example, after processing all the data related to all the product's user with an unsupervised algorithm, 14
it might come up with a way to group your users into two groups. 15
After inspecting and comparing these two groups, you might realize that Group A is in a geographic 16
location of Asia, for example, and Group B is in a geographic location of Europe. 17
Later, it's up to you to make furthermore decisions on this system. 18
So whenever we have some kind of big data and we don't know how to group there, we just ask the machine 19
with unsupervised learning to come up with somehow grouping the data.