In the last lesson, we spoke about supervised learning as a form of machine learning. 1
So this is where when you're training up your computational model, you're providing labeled data. So your 2
data is clean and everything that you feed into the model has a label that the computer can understand. 3
So, for example, you could feed in loads and loads of pictures of flowers and each flower has a string 4
that labels what it is, 5
so roses or chrysanthemums, or whichever. 6
Now, we're going to talk about unsupervised learning and this is when the teacher goes out of the room 7
and all the children go crazy. 8
No, I'm just kidding. 9
It's one of the other major types of machine learning. Now, with unsupervised learning, instead of providing 10
training data where each and every piece of data that you feed into the model is clearly labeled, 11
instead, you're just feeding it a whole bunch of data, and you want the model to work out some sense in 12
that data set. So it learns to find structure in your unstructured data. 13
So in unsupervised learning, you might have a whole bunch of data that looks like this. And you're not 14
really telling the computer what all the data means, but instead, you want the computer to simply learn 15
and be able to see this data seems to be organized in a particular fashion, and you can actually divide 16
them roughly along this line, 17
and the computer can see the structure within the data so being able to differentiate that this might 18
be two separate groups of data, having something more in common with each other than the other group. 19
So one type of unsupervised learning is called clustering and this is where the computer learns and 20
looks at the data and all of its various features, and figures out that all the data belongs in separate 21
clusters, instead of a across-the-board random set of crosses. 22
We actually have discrete clusters that probably can be categorized as grouped in clusters. 23
So let me illustrate this with a real-life example. 24
I think you'll make it a lot more clear. 25
Now, let's say that you work on the engineering team at Facebook. 26
Now, one of the problems that you're tasked with solving is, how do you get more users to engage more 27
on Facebook? 28
This is the dilemma that real-life Facebook engineers are faced with every day. 29
So one of the ways of increasing engagement is by putting people in touch with friends that they didn't 30
know were on Facebook or friends that they have an added as a friend yet. 31
So how can you do that if they're not searching for those friends? 32
Well, if you look into the data on Facebook, you might be able to see that this person is connected to 33
this person. 34
So that could be through the fact that they have both added each other as friend or maybe, you know, 35
the guy on the left has tagged the woman on the right or various other activities that show this connection. 36
Now, if you map this out to all the users, you'll be able to see all of their interconnected relationships 37
and you can see which users are linked to whom. 38
Now, even as we look at this using the human eye, you can sort of see clusters appearing, right? 39
So maybe this is a cluster or a friendship group 40
and this is another cluster friendship group. 41
So using clustering and using unsupervised learning, just by looking at the Facebook raw data, the machine 42
learning algorithm is able to tell who probably belongs in certain social groups all without any sort 43
of training data. 44
You know I didn't say that these and these friends are in my close inner circle or those and those friends 45
are in my basketball team or whichever, 46
but all of this data is worked out based on these patterns that are recognized by the computer. 47
So what can you do using clustering? What is a application of this type of machine learning? 48
Well, let's zoom in on this particular social circle. 49
Now, you can see that this lady here is not yet linked to these two people, right? 50
Even though she seems to belong in the same social group. So she probably knows those two people, but 51
just hasn't yet added them as a friend. 52
So if you go onto Facebook, one of the most prominent things that you see nowadays at the top is this 53
bar where it says, "People You May Know," and the Facebook machine learning algorithm is using clustering 54
and using unsupervised learning to look at these maps of social networks figuring out who belongs in 55
which social circles and the people that you might know who are also in the same circle as you, but you 56
haven't yet added them as a friend. 57
So this is how it figures out some of those people who you might want to add as friends. 58
Now, if you add more people as friends, then you have more interactions, 59
you see more of their pictures, increasing engagement on Facebook. 60
And there we go, 61
job done using machine learning. 62
So this is called clustering and it is a type of unsupervised machine learning. 63
Now, there's quite a few other types of unsupervised machine learning, but it's beyond the scope of this 64
talk at the moment. We're currently working hard on entire suite of machine learning lessons in order 65
to teach machine learning in a way that's easily understood and we can go into deeper topics like reinforcement 66
learning, back propagation, and lots, lots more. 67
But for now, we've spoken about the two major types of machine learning which are supervised learning 68
and unsupervised learning. 69
Now, in the next lesson, I'm going to talk to you about one of my favorite types of deep learning which 70
is reinforcement learning. 71
So I'll see you on the next lesson.