I welcome to chapter 6.1 which is basically a machine learning crash course overview.
All right.
So let's get started into this so what is machine learning now.
Machine learning has been synonymous to artificial intelligence because basically it's a field of study
that basically studies how algorithms or software actually learns from data.
So basically as I said some field or field in artificial intelligence that uses statistical techniques
to give computers the ability to learn from data without being explicitly programmed and explicitly
means like if this is dad and that is that basically a hard look up table of criteria machine learning
does not do that.
It learns from the data learns it's one model of how it should be answered and basically over the last
five to 10 years maybe even 15 years or so machine learning has exploded there.
Basically a number of masters and BSD programs all over the world have specializations in machine learning
now it is and this has mainly been brought brought born because processing power and also from GPS use
has basically caught up to the process of intensive intensity required for machine learning.
So there are four types of machine learning with neural networks being basically one type belonging
to us.
A subtype belonging to one type of DS for these four are basically supervised unsupervised self supervised
and reinforcement learning.
And I'm going to talk a little bit about each one so fiercely supervised living now supervised living
is by far the most popular form of E.I. and well used today.
And they'll being machine learning basically because it's relatively easy compared to other things to
implement.
All they need is labelled data set and we feed this data set into machine learning model algorithm and
it develops a model to fit this data to some outputs.
So basically it's like an example here is let's see if we have 10000 emails that are labeled spam 10000
that are not spam and we give this to a model.
Basically we get the text and Miss riskless subjects and from the sender of the email and now the e-mail
story the machine learning algorithm is now going to figure out what is spam based on that.
So we have input data being an e-mail model that we just trained and it outputs but it's or not.
So that in a nutshell is supervised learning.
And here are some examples of supervised living in crappy division.
Basically it's used heavily in image justification even object detection and segmentation.
Basically all of these involve feeding some label data into our depleting model and treating it and
getting a model that is accurate enough to take unseen data and classified them correctly.
So what about unsupervised learning now unsupervised learning learning is concerned with finding interesting
clusters Indian data and it does so without any hope of data labeling.
So you just feed some data into it and the unsupervised learning algorithm basically finds interesting
patterns and clusters in the information
it is actually very important in data analytics when you're trying to understand vast amounts of data
of that data.
Basically when you have huge data sets with huge number of columns and rows and different information
using unsupervised learning can help you understand very quickly what is important in your data.
This is a best example of it here.
Go back to it.
So now imagine we have basically meats of food items here.
And we give it as a seamstress pictures here and we'll give it to an unsupervised machine learning algorithm
and it's going to actually pick it close to what interests you and I'm willing to bet close to what
is going to be the first tree I trusted to baby does it's a meets it's that is interesting pattern unsupervised
machine learning algorithms help us pick out.
So what about self supervised learning.
Now sylphs revised learning is the same concept as supervised learning.
However that data is not labeled by humans which is pretty interesting so how it is level is generated.
Basically it's done using heuristic algorithms.
Example also includes an A good example of that is basically trying to predict the next Freman a video
given the previous frames.
That's a very good example of self supervised learning.
We're not going to deal with any self-sacrifice or unsupervised learning in this class but it's good
to know.
If you want to have an overview of machine learning what they're about.
And lastly we have reinforcement learning that reinforcement learning is potentially very interesting.
However it's still in its infancy and it's still a bit tricky to actually do.
I actually did a couple of courses classes on this in my university in Edinburgh and it wasn't that
fun.
Was actually very challenging but once I got it working it was actually quite fun quite cool.
So the concept is pretty simple.
However it's that simple to implement.
But we basically teach the algorithm something by giving it bad examples or penalties against something.
So it's of like learning to play games.
It's a very good example of reinforcement learning.
You're basically trying different things and getting punished or dying or losing points for something
until you come up with a strategy where you're basically minimizing your loss.
That is what reinforcement learning technically is.
So in machine learning it and machining in supervised machine learning is a basic tenet process which
you follow in every basically every using of every algorithm whether it be deplaning convolutional and
that's SVM.
Blah blah blah.
They all follow this pattern.
So in step one you obtain a label dataset.
Step two is split to say the set.
This is very important into a trining portion and the validation or test portion noted is technically
a little difference between the validation and test portion.
However for all intents and purposes which is about Free's I shouldn't be using but whatever.
Basically validation and test push is basically the unseen data.
Your model never sees this data model only sees the training data and we test performance on the validation
or test push and test tested assets.
So this in step 3.
We take this training data set that we split from the original.
And we feel it's our model.
So model takes us still and isn't inputs all labels and basically loon's tries to figure out patterns
how do we predict this.
How do we know what this is after some time it develops a fully trained model and so forth.
Basically we run this model in our test validation dataset to see how effective it is.
So here's some machine learning terminology that you're probably going to hear in this course and it
will basically target data with all this if we is to the ground troop levels technically in programming
languages.
You'll see that refer to as the y o In mathematics the Y labels X being the training data set.
Sorry about that.
And prediction basically being what all models predicted from some input data classes will be basically
the categories of your data.
So if you were talking about the hand-written digit amnesty the set there were 10 classes there would
be zero 1 2 3 4 5 6 7 8 9 aggression or phrase do when you're in classes we are operating basically.
This image belongs to this last regression.
We're putting basically a continuous value digit number.
So that's say we taking some inputs and trying to predict someone's height or weight that would be a
regression model.
And as I mentioned before invalidations last tests yes to can be different.
But in early Christou unseen data that we test our tree and model on.