Hello, guys, and welcome and welcome to another of my courses, they squiz topic would be data science
as well as machine learning, but principal data science.
So before we even start the course, I'd like to thank you all for joining this class.
Mostly, I'm really glad to be your teacher and help you learn more about this subject.
So basically, the way I created discourse, I tried to be the more interactive possible.
I tried not to create a long course, but take everything that I knew about the topic and put it together
and create the shortest course possible with the maximum amount of knowledge in it.
So this way, the number of people who will complete the course will be very, very high.
And for you guys, it's going to be way easier to complete a course that is a bit shorter like this
one.
Then, of course, that is wait longer.
Honestly, I'll be honest, this is not an easy topic since we are talking about programming language.
We're talking about the science as well as machine learning.
But once again, I'm pretty convinced that this course will give you guys all the basics to have a complete
introduction to this topic.
Yes, by the end of this course, you guys will not be professionals.
And yes, you will need a lot of practice to be able to understand completely this topic.
But once again, when you will have completed this course, we'll have all the basic knowledge to be
able to, let's say, start in the school.
So what is the goal of this course?
First of all, my goal will be to teach you the basics of data science.
So in this course, we are going to talk about what is there a science, how it works, everything that
is around data science as well as machine learning.
So once again, I'll try to guide you step by step and really put putting everything together.
So this way you guys will not be lost and understand why we use some techniques or why we use some tools
and not others.
We are going to learn to work with multiple tools.
Well, in this course, you're going to see we going to learn multiple tools.
And the main programming language in this course would be Python.
Basically, the tools that we are going to work with could be multiple matplotlib, seabourne numpty,
pandas and many others that we are going to see in this course.
Those are all very interesting tools that we are going to learn in this course.
So by the end of this course, once again, you guys will have all the knowledge of those tools, which
is pretty cool.
Um, you guys will as well understand the basics of machine learning.
So this course is really here to help you understand what is machine learning and all the basics around.
It is, of course, is a lot of about data science, but there is well, there is a link between data
science and machine learning.
It's really, really close.
And we are going to talk about both topics.
The course content, the way I divide it, it is pretty much, well, simple.
First of all, we got to talk about the introduction and we'll have a small introduction to data science
and everything.
We are going to talk about the basic statistical knowledge.
So in this case, we'll have a few classes about statistics.
And while we talk about data, we're going to talk about statistics and different things that you guys
will need to know to be able to, well, understand data science, then we are going to talk about different
tools.
As I said before, the tools that we are going to use here will be no Skype by matplotlib.
It's hard to pronounce and Seabourne as well.
So each of those tools we are going to get inside of it will give you guys a complete introduction to
this tool.
And we are going to talk really in-depth about each of those tools and practice some exercise on those.
Then we are going to talk about mathematics because the data science is really, really made of mathematics.
Mathematics are like the core of all this.
So we are going to have a big part of the course that is really about mathematics.
Once again, it's not a huge vote because this is just an introduction.
But we are still going to talk about some mathematics, for example, the basic mathematics for machine
learning, basics of algebra and some other calculations that we are going to talk about.
So you will see it's not something that is impossible to do.
But if you have a basic maths background, you guys should be good to go for this part of the class.
Then we'll have an introduction to machine learning.
So once again, we're going to talk about machine learning, about different algorithm algorithms,
sorry, that exists in machine learning.
We're going to talk about classification and many other things, which is pretty cool.
We then will.
Talk about regression.
So what is regression when it's used types of regression and, well, for example, linear regression
and some other methods that we can use, you'll see it's not something that is that complicated.
Once again, you need to understand it to be able to go further in this field.
Then we'll have introduction to some other tools.
In this case, we'll talk about Carus and the Bispebjerg once again.
I'll give you a complete introduction to those tools as well, and we will practice a little bit with
them.
So once again, as I said, discourse.
Yes, it's a bit long.
But once again, the goal here is really to give you a complete introduction of the subject.
The subject is not easy.
Once again, I think that with discourse, you guys will be able to understand at least the basics of
data science as well as machine learning.
So what will you be able to do by the end of this course?
First of all, you will understand all the basics of the science, so you will understand what is the
science, how it works and the principle.
What are the main tools that you guys can use to be able to understand that science?
And if you guys start practicing right through discourse, I'm pretty sure that you can create really,
really great things.
Um, next thing, we'll learn how to work with the python for the design.
So, as I said, the main programming language for discourse would be in Python.
So you guys will understand.
Well, if you guys are beginners in Python, you will get a step further in this programming language
and you will understand some different programs, small programs that you guys can write in Python to
be able to use them for data science as well as machine learning, which is pretty cool in my opinion.
And this way, you guys will be able to practice your programming as well.
And also you will be able.
Well, the last thing is that you will be able to work with different python tools.
So, as I said, the many of the tools that we are going to learn in discourse works with Python.
So you will be able to use all those tools to perform data science things.
For example, if you want to make your research about a certain topic or anything that requires the
use of data science, you will be able to do it by the end of the SCHAUS or at least have the basic
knowledge to be able to create your plan and then do it by yourself.
Not only this, for example, you have a personal project that you want to do in this field, in data
science or machine learning.
I'm pretty sure that this question really will really give you a strong basis to be able to start this
little project that you guys want to do, subsets of that.
I think this is what this course is all about.
And once again, I'd like to thank you all another time for taking the course with me.
I'll be really, really glad to teach you.
Well, it's an honor for me, actually, to be able to teach you this topic.
It's not easy.
It's not an easy topic.
Once again, you'll see by the end of this course, you should pretty much master all the basics of
this topic and have a good basis to be able to launch your career or simply launch your hobby in this
field.
So that's it for this introduction, guys, into all in our first class.