so you want to learn artificial

intelligence then this video is for you

I'm going to provide you with a complete

roadmap that I would follow if I had to

start over today on my artificial

intelligence journey and now for context

I started studying artificial

intelligence back in 2013 10 years ago

and over the past years I've been

working as a freelance data scientist

helping my clients with various

end-to-end data science and artificial

intelligence Solutions and applications

I also share all of this knowledge and

my journey on this YouTube channel which

as of today has over 25 000 subscribers

and at the end of this video I will also

provide you with a resource completely

for free where you can follow all of

these steps to complete roadmap even

with training videos and instructions so

make sure to stick around for that and

now before we dive into the seven steps

that I would take today to go from

beginner all the way to monetizing my

data and AI skills it's important to

provide some context on what is

currently going on with the AI hype

because I see a lot of new people

entering the field and for a good reason

because the AI Market size is expected

to grow up to 20 volt by the year 2030

bringing it all the way to nearly 2

trillion US dollars so it's really one

of the best opportunities I would say

right now to get into because we're

still early we're still at the beginning

of this AI Revolution and also with the

release of these pre-trained models from

open AI it's now also easier than ever

to enter the field but that said that is

also where a lot of the misunderstanding

and just wrong expectations arise from

because I see a lot of people online as

well as on YouTube explaining like how

you can quickly start for example your

own AI automation agency and while there

are great tools already online out there

like both press and stack Ai and

flowwise which I also made a video on

where you can quickly spin up prototypes

and and simple Bots and even can get a

little bit more advanced don't get me

wrong you can definitely build some

great Solutions with that but if you

really want to learn artificial

intelligence and build applications that

companies can count on and build upon

then you really have to understand the

coding part the technical part really of

it so that's really where our starting

point should be for you and for your

learning path figuring out hey do I want

to just learn how to use these no code

Loco tools already available or do I

really want to learn artificial

intelligence and with that said there is

also just a general misunderstanding I

believe of what really AI is because AIS

is such a large umbrella term and it's

also nothing new it's been around since

the 1950s but right now with the chat

GPT hype and the open AI models people

think AI is that really if we look at

what artificial intelligence really is

it's like I've said a real big umbrella

term with various subfields so for

example within artificial intelligence

which is here explained as programs with

the ability to learn and reason like

humans machine learning then we have

deep learning which is another subset

focusing on neural networks and then we

have the field of data science but in my

work as a data scientist I use

artificial intelligence I use machine

learning and I also use deep learning

it's a lot more than what people think

the first real question that you gotta

ask yourself is do you want to be a

coder and now there's no right or wrong

answer here there are plenty of

opportunities right now and also in the

future for both Pathways for both local

NOCO tools and building custom

applications but you just gotta be aware

of the pros and cons to both of the

sides and not to be totally clear this

roadmap is for people that really want

to learn AI with the depth of

understanding really learn the technical

side of things and now if you've decided

that that is not for you that's of

course totally fine like I said there's

no right or wrong but then if you want

to still want to do things with AI then

I recommend starting out by checking out

both press like I've set or stack AI

which are excellent resources or you

could check out my video on flowwise

here on YouTube where I show you how you

can get started with a local NOCO 2 as

well completely for free but if you do

decide that you want to join the Dark

Side and become a coder then let's

proceed with the next steps my Approach

is quite different from anything else

you will find online and now why is that

and what I typically see online is you

have two ends of the the Spectrum

basically where on the one hand you have

the people talking about these low code

and no code tools not really getting

into the specific the theoretical part

and then on the other hand you have the

more classical approaches towards

artificial intelligence and machine

learning where people really get into

the mathematics and the statistics

giving you road maps where you really

have to get theoretical first I'm a firm

believer of learning by doing reverse

engineering things that people have

already done putting in practice and

then trying to fill in the gaps now the

technical roadmap that I'm going to

provide to you will really focus on the

fundamentals that you need in order to

get started in either artificial

intelligence data science or anything in

between like I've said I've worked in

all of these fields over the past 10

years and I've really identified the

core techniques workflows and tools that

you need in order to get started

regardless of what you want to do so

this will work for you if you just want

to build applications with large

language models and Lang chain for

example but it will also work if you

aspire to become a data scientist or a

machine learning engineer now the actual

first step that I would focus on on my

AI Journey would be to set up my work

environment now what does this mean so

python is the go-to language that we

have to learn if you want to get started

in AI or in data science but the thing

is

Titan if you start to follow these

tutorials online videos training videos

courses even you can quite quickly

understand Python and how it works

because it's one of the easiest

languages to get started with but I

found in my personal Journey that

there's this initial bump where you see

things online and you see people run

some code but then you are missing some

information on okay but how do I now

actually do this on my laptop on my

computer

and I would really focus on this first

setting up an environment on your laptop

on your computer where you have an

application a program and a python

installation that you are confident with

and now I have a specific approach that

I take over here within fias code and a

lot of people seem to like that so make

sure to check that out in the resources

but this really is step one they're

getting accustomed with that and that

brings us then to step two which is

actually getting started with python

it's like I said the most important

language this is going to be your tool

that you're going to build these

applications in now if you're new to

programming at all I would first focus

on the fundamentals of programming which

I will have resources to but then

quickly transition into learning the

basics of python and then specifically

some libraries that are very useful for

AI and data science in particular so

these would be for example the numpy AI

Library the pandas library and the matte

plus lib library now these are all

libraries that you can use to do data

manipulation data cleaning creating

visualizations this is really your

starting point for starting to work with

data because in the end all AI

applications all AI tools are created

from data with data so being able to

work with data and turn raw and

unstructured data into information into

valuable insights that you can actually

do something with is is really at the

core of of artificial intelligence and

now step three would be to learn the

very basics of git and GitHub now why is

that some would argue that that would be

a little bit more advanced and it's not

required in the beginning but what I've

found especially with artificial

intelligence and also the video

tutorials that I make is that a lot of

examples online people will make that

code available via GitHub but you have

to understand kind of at the very base

sick how these tools work because that

allows you to easily copy and clone is

what they call it tutorials that brings

us to step 4 which is working on

projects and building a portfolio and

for this it's convenient if you already

know how to use git so you can download

some projects download some code from

from other people and then try to

reverse engineer it to me that really is

the best way to to Learn Python to get

good to actually understand holistically

what a project looks like how people are

structuring their code and trying to run

it and then you don't understand what's

going on but then trying to reverse

engineer so it's really like beginning

with the end in mind and then trying to

change things and see how that affects

the different outcomes and this also

provides you with an opportunity to

explore what it is specifically that you

like about artificial intelligence all

the areas we've discussed computer

vision natural language processing

machine learning he here you really find

out okay these are all the kinds of

things that I can do and this is really

what I like to do and then as you're

working on these projects selecting them

picking them you there will be a lot of

gaps and and things you don't understand

and that would be a good point if you're

interested in that to find specific

pieces of information or courses to help

you with just that and now when it comes

to projects probably the best place to

start if you want to learn more about

data science and machine learning is

kaggle so kaggle is an excellent

resource that you can go through and

they host machine learning competitions

here so you can see all kinds of

requests and you can even win prizes so

this is one from Google and the cool

thing here is if you click on the actual

competition you can also actually have a

look at submissions that people have

made so here you can see an entire

notebook from someone

that is trying to solve this problem for

Google all with documentation and and

even the code so this is such an

excellent learning resources source that

you can go through like I said there are

plenty plenty of resources available on

here but if that's not for you machine

learning data science if you want to

just explore large language models in

open AI for example right now then I

recommend to check out my GitHub

repository on Lang chain experiments so

I also have videos on my YouTube channel

for that but here on the repository

that's why it's good that you at least

understand the basics of git and GitHub

so you can take this code know how to

work with it so here are some cool

examples of how you connect can create a

YouTube bot that can summarize a video

or even a slack bolt or a Ponders agent

that can ask questions and answer

questions about large data tables and

now if you're really serious about

learning artificial intelligence and

data science and another great resource

that you can check out is Project Pro

which I've recently discovered so

project Pro is a curated library of

verified and solved end-to-end project

Solutions in data science machine

learning and big data so overall this is

just an excellent resource with with so

much information and all the projects on

here that you can pick from all from the

various fields are all created by top

industry experts from leading tech

companies so what I really like about

this is first of all you have about 3

000 free recipes that like anyone can

check out but if you get to the

subscription and that is why it really

gets interesting you have access to 250

plus end-to-end projects so you can

really like go in here and see okay what

is it that you're working on so maybe

it's data science and you want to

specialize in machine learning and you

go in here you literally have all kinds

of projects and this is not only a great

resource for you to learn from because

you will have complete video

walkthroughs 24 7 support and you can

ask questions and and you can even

download all of the code so literally

the entire project will be made

available to you so it's a excellent

Learning Resource but also for me

personally working as a freelance data

scientist this can also like really help

me in my professional work that the

projects that I take on so for you that

could either be in your job or in future

jobs freelancing whatever you really

have a library that you can pick from

that can really give you that extra kind

of confidence you need for example to

take on a project now like I've said

really you see video instructions you

can go through everything and then also

download the code so this really is a

great resource that you can check out

and if you want to learn more about this

I will leave a link down in the

description and project Pro also has a

YouTube channel which you can subscribe

to if you want to stay in the loop learn

more on that and that brings us to step

five which is picking your

specialization and sharing your

knowledge so right now you understand

the fundamentals of python you have a

work environment and some some efficient

workflows that you can follow you also

have some project experience so now you

get a little bit more clarity of what it

is that you want to do within the world

of AI or data science or machine

learning so this would be the point

where you pick a focus area you

specialize you try to learn more and

also what I really would recommend and

what I would do is to start sharing your

knowledge so you could do this through a

personal blog you could do this through

writing articles on medium or towards

data science or you could even

potentially like I'm doing share your

your knowledge on YouTube and by doing

so you're not only contributing to the

collective knowledge on AI and data

science but it's also an essential

method for you to strengthen your own

learning because in doing so in

explaining Concepts that you're working

on that you're learning to to someone

else you really start to identify the

gaps within your understanding and this

again allows you to fill in those gaps

accordingly and really focus on some

specialized learning versus just going

through course after course after course

and then step six would be continue to

learn and upskill because now that you

have Clarity on your specialization and

kind of the direction that you want to

go and you also start to identify these

gaps within your own understanding

it might be time for you to for example

focus on math focus on statistics if you

want to become a better machine learning

engineer or a data scientist but if

you've decided to go with the large

language model and generative AI route

you might identify that you need some

software engineering skills actually

really start to understand how you can

work with with apis and create

applications and that's like I think the

main main message that I wanna want to

provide you with with regards to this

roadmap and and my Approach is that it's

everyone's journey is is unique and

depending on what you want to do with AI

there's a specialized learning path for

you specifically so my goal is to really

provide you with the tools and

techniques to quickly get going

get your hands dirty identify problems

work on projects and then fill in those

gaps and then finally step 7 would be to

monetize your skills now this could

either be through a job this could be

through freelancing or this could be

through building a product but where the

real Learning Happens is is when there

really is some pressure onto it so it's

all fun and games when you're trying to

explore this within your free time

following some courses following some

tutorials but when it's your boss or

when it's a client that's that's

breathing down your neck for the

deadline that is where you really push

yourself that is where you really get

creative get resourceful and try to

absorb and learn as much information as

possible to just get the job done and

that's it those are the seven steps that

I would take today if I had to start

over completely from scratch on my AI

Journey and now another bonus tip that I

can provide you which will make a great

difference is surround yourself with

like-minded individuals who are on the

same track the same path as you who

share the same interest where you can

bounce ideas off where you can share the

latest news and tips with and in order

to facilitate that for you as well I

have an exciting announcement because

today I will officially be releasing my

free group called Data alchemy that I

would like you all to invite you this

will be a group where I not only share

the complete and entire roadmap that I

just shared with you with all the links

resources tools it will also be a hub

your go-to place to navigate the world

of data science and artificial

intelligence and everything that's going

on and happening right now within this

rapidly changing field so if you're

serious about learning artificial

intelligence and data science and you

also also want access to not only this

entire roadmap but additional courses

and resources then make sure to check

out the first link in the pinned comment

below this video and then I look forward

to seeing you in the group

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