And we'll come back to the class of, of course, about the complete introduction to the science with
Python.
So in the past few classes, we talked about the many tools that you guys can use to be able to perform
data science with Python programs.
We talked about pendas, we talked about Seabourne, known by Skype and other things.
And right now we are going to talk about another topic that is really, really related to that science
and that requires a lot of data science to be able to be performed.
And this is machine learning.
So basically, in the next few classes, we are going to talk about machine learning in general.
What is it, how it works, the different types of machine learning we used to have?
Well, I'll try to go as in-depth as possible and keep it as simple as possible as well.
So not making nothing that is too complicated to help you understand.
And also, we're going to talk about some of the algorithms, algorithms that can be used with machine
learning.
So this way you guys will understand some of those algorithms and how they work and basically how you
can program them with the use of Python.
So let's start.
All right.
So basically, what is machine learning?
So the first thing that we need to do is really understanding what exactly is machine learning and before
even going further in the topic.
So basically, there are plenty of definitions for machine learning.
So a lot of them exists.
For me personally, machine learning would be the use of AI to provide system systems, the ability
to automatically learn.
So we say we'll use artificial intelligence that is based on data science to help machines, to help.
But your system basically not a machine.
It's a system to help your system learn and perform tasks based on past experience.
So basically, let's say you take you create a system that will automatically drive.
So basically to teach your system how to drive automatically, you give him a lot of videos, for example,
or you will put him in a lot of experiences.
So basically you will make him experience the road and many other things, and the system will simply
adapt to do well to to your learning and adapt to the situation and then bring the outcome that you
want to have at the end.
So basically, you will tell your machine, I want this outcome and this is well, this is the income
and I want this outcome from it.
And that basically the goal of the machine is finding the best map possible.
And this would be a supervised learning.
We'll talk about it a little bit later.
But this is one type of supervised learning that exists.
So basically this can be machine learning.
Next thing is machine learning can be the science of making computers learn and act like humans.
So basically what we're trying to say now is really what this is really more advanced.
So we're talking here about giving not a conscience to computers, but simply make computers understand
their environment like human beings to basically understand faces understand.
And this is really a more advanced machine learning at basic points.
It's really helping computers perform certain tasks.
Basically, let's say you want a machine that is able to recognize faces like, for example, on your
phone or on your phone.
You have a machine learning applications or a I that will be able to recognize faces if we create.
Well, if we say that this is a machine, if we implement and it's a machine learning system, but it's
going to do it's going to start learning from from its experiences.
So we will learn each time that it will see a face.
It will add it to its database and to learn more and more and more and more and basically to make those
types of systems how it works.
As I said, you have an income.
So basically you have your income where your starting point and your outcome, let's say you want to
recognize a face.
So we will create a system and give the system a lot of faces and the system will get used to recognize
faces and it will create a map to recognize faces the best way possible.
So basically, this would be your machine learning.
This would be your machine learning system.
And you have plenty of algorithms that will help you achieve this goal unless there is the use of algorithms
to allow analyze of data to make a prediction about something in the world.
So basically, you could be used for this as well.
So let's say, for example, on the stock market, you want to predict the movement of a certain stock
in a certain direction.
So basically you can use the machine learning for that.
So what they will do, it will simply analyze the chart.
And the what I'm saying analyzing it is going to be a pure technical analysis to be able to analyze
all the aspects of the chart, to analyze absolutely everything, all the movements on each segment.
Well, it depends how you programmed your your system, but it can be really, really, really advanced
and basically with.
All this information, it will be able to make the best purchases possible because to optimize the max
and those are things that a machine can do, that a human being will not necessarily be able to do because
the calculations that are behind a behind the scene of all this are really, really advanced.
All right, so some examples of machine learning, so as I said, there are plenty of examples of machine
learning.
You can see them all right here so we can have image recognition.
So basically, there are plenty of systems that exist in the world used for security purposes or any
other purpose to be able to recognize well, for facial recognition, for example, or simply image
recognition and just to recognize an image.
Those are eyes.
But to be able to be created, we have used machine learning systems to be able to make the creation
of those systems speech recognition.
So one of the most popular speech recognition that exists will be, for example, on Google.
So when you go on Google and you go on Google Docs and you want to write down a text, they have a speech
recognition system that you can talk and it will automatically generate a text traffic prediction.
This can be, for example, well, for engineering purposes.
So, for example, you want to you want to estimate the traffic or any other thing it can work as well
for detection.
So basically you can detect frauds on millions of transactions.
So those are really advanced systems as well that can detect fraud and on millions of transactions,
financial field.
So, as I said earlier in the stock market.
So to be able to predict the movement of a stock or a ETF or whatever, it's possible with the machine
learning as well.
So here you can find plenty of other examples of, for example, self-driving cars, simply the email
spammers and malware filtering.
So basically on your email, on your email it to choose an email to go to spam or to go directly to
your email base.
Basically, it's machine learning.
So there are plenty of things that use machine learning.
It's all around us.
We just are not used to it or we simply don't know what is it.
And this is why we are maybe not.
Well, we maybe just don't understand what is it to be able to see it.
All right.
So let's thing would be, what are the different types of machine learning that exists?
So there are three types of machine learning that exists.
We have supervised learning and basically when we talk about supervised learning, it could be training.
Well, if we want to understand and have the definition of supervised learning, it will be training
an algorithm and pick the model that will be the best to predict some outputs based on inputs of data.
So, as I said, basically you have an input and you want to have a certain output.
So you will train your system to be able to predict this output.
So as is an example, we can think of simply facial recognition on pictures.
This could be a good way.
So basically how all this would work behind the scene.
So the system will receive multiple inputs and outputs.
And just the job of the system is pretty simple.
It will be to map all well, to be able to create a map or basically a route from the inputs to the
output.
And it has to be the most optimized possible.
And the more the system will be trained in our case, well, the more it will be effective.
So basically, the more you train your system, the more you give him information to be able to understand
this information and transform it, the more the system will be good and will find the right answers
and bring you to the right output.
All right.
Same thing.
Be unsupervised learning.
So basically the difference between supervised and unsupervised is that there is no outcome to predict
in the unsupervised learning.
So basically this type of learning is done with unlabeled data, if we can see this week and to find
uncovered hidden structures.
So basically, it's harder to use and is, well, less used.
Obviously, it's not as used as supervised learning and usually it can be used before the supervised
learning.
So simply to well.
To provide information about the potential outcome and how to reproduce it.
So basically, if you don't know the outcome, you lose, you will use unsupervised learning simply
to maybe have an idea of how you want the outcome to be or how you want to get the outcome.
And after that, it's supervised learning.
You have a certain idea of the outcome.
You can try to train your machine to be able to reach that outcome.
Then the last thing will be reinforcement learning.
And basically reinforcement learning is all about teaching suitable actions to maximize results in a
particular situation.
So in other words, basically what you want to do is really train your machine to be as optimized as
possible so it will learn from errors, as it says right here.
So basically you want to have something that is well optimized, the max and will simply be able to
adapt in any type of situation.
So basically reinforcement learning, there is no interest, but their enforcement agent decides what
to do to have well to to optimize the realization of the task.
So basically, we can see some examples right here so we can talk about real time decisions.
We can talk about skill acquisition.
We can talk about robot navigation.
So those are really more advanced things.
And basically you have a system that is already in place.
And what you want to do with this system, you want to optimize it.
So basically when you want to optimize your system, you will not use supervised or unsupervised learning.
You will really use reinforcement learning, which is the thing that is used to be able to optimize
to the max your system.
So right now, I hope you guys have a basic and well basic introduction to what is exactly machine learning
and how it works.
And in the next classes, we are going to start talking about some of the machine learning algorithms
that exist.
So that's the first thing that goes into all the next class.