0:01 so you want to learn artificial
0:04 intelligence then this video is for you
0:06 I'm going to provide you with a complete
0:08 roadmap that I would follow if I had to
0:10 start over today on my artificial
0:12 intelligence journey and now for context
0:14 I started studying artificial
0:17 intelligence back in 2013 10 years ago
0:18 and over the past years I've been
0:20 working as a freelance data scientist
0:22 helping my clients with various
0:25 end-to-end data science and artificial
0:28 intelligence Solutions and applications
0:30 I also share all of this knowledge and
0:31 my journey on this YouTube channel which
0:34 as of today has over 25 000 subscribers
0:36 and at the end of this video I will also
0:37 provide you with a resource completely
0:39 for free where you can follow all of
0:41 these steps to complete roadmap even
0:43 with training videos and instructions so
0:45 make sure to stick around for that and
0:47 now before we dive into the seven steps
0:49 that I would take today to go from
0:51 beginner all the way to monetizing my
0:53 data and AI skills it's important to
0:55 provide some context on what is
0:57 currently going on with the AI hype
0:59 because I see a lot of new people
1:01 entering the field and for a good reason
1:03 because the AI Market size is expected
1:07 to grow up to 20 volt by the year 2030
1:09 bringing it all the way to nearly 2
1:12 trillion US dollars so it's really one
1:14 of the best opportunities I would say
1:16 right now to get into because we're
1:18 still early we're still at the beginning
1:22 of this AI Revolution and also with the
1:24 release of these pre-trained models from
1:26 open AI it's now also easier than ever
1:29 to enter the field but that said that is
1:32 also where a lot of the misunderstanding
1:35 and just wrong expectations arise from
1:37 because I see a lot of people online as
1:39 well as on YouTube explaining like how
1:40 you can quickly start for example your
1:44 own AI automation agency and while there
1:46 are great tools already online out there
1:48 like both press and stack Ai and
1:50 flowwise which I also made a video on
1:53 where you can quickly spin up prototypes
1:55 and and simple Bots and even can get a
1:57 little bit more advanced don't get me
1:58 wrong you can definitely build some
2:01 great Solutions with that but if you
2:02 really want to learn artificial
2:05 intelligence and build applications that
2:08 companies can count on and build upon
2:10 then you really have to understand the
2:12 coding part the technical part really of
2:14 it so that's really where our starting
2:17 point should be for you and for your
2:19 learning path figuring out hey do I want
2:22 to just learn how to use these no code
2:25 Loco tools already available or do I
2:26 really want to learn artificial
2:29 intelligence and with that said there is
2:31 also just a general misunderstanding I
2:34 believe of what really AI is because AIS
2:37 is such a large umbrella term and it's
2:39 also nothing new it's been around since
2:42 the 1950s but right now with the chat
2:45 GPT hype and the open AI models people
2:49 think AI is that really if we look at
2:51 what artificial intelligence really is
2:54 it's like I've said a real big umbrella
2:57 term with various subfields so for
2:59 example within artificial intelligence
3:01 which is here explained as programs with
3:02 the ability to learn and reason like
3:04 humans machine learning then we have
3:06 deep learning which is another subset
3:08 focusing on neural networks and then we
3:11 have the field of data science but in my
3:13 work as a data scientist I use
3:14 artificial intelligence I use machine
3:17 learning and I also use deep learning
3:19 it's a lot more than what people think
3:21 the first real question that you gotta
3:24 ask yourself is do you want to be a
3:27 coder and now there's no right or wrong
3:28 answer here there are plenty of
3:31 opportunities right now and also in the
3:33 future for both Pathways for both local
3:35 NOCO tools and building custom
3:38 applications but you just gotta be aware
3:41 of the pros and cons to both of the
3:43 sides and not to be totally clear this
3:45 roadmap is for people that really want
3:46 to learn AI with the depth of
3:49 understanding really learn the technical
3:51 side of things and now if you've decided
3:52 that that is not for you that's of
3:54 course totally fine like I said there's
3:55 no right or wrong but then if you want
3:58 to still want to do things with AI then
4:00 I recommend starting out by checking out
4:02 both press like I've set or stack AI
4:04 which are excellent resources or you
4:06 could check out my video on flowwise
4:08 here on YouTube where I show you how you
4:10 can get started with a local NOCO 2 as
4:13 well completely for free but if you do
4:15 decide that you want to join the Dark
4:18 Side and become a coder then let's
4:20 proceed with the next steps my Approach
4:23 is quite different from anything else
4:25 you will find online and now why is that
4:28 and what I typically see online is you
4:30 have two ends of the the Spectrum
4:32 basically where on the one hand you have
4:34 the people talking about these low code
4:37 and no code tools not really getting
4:39 into the specific the theoretical part
4:42 and then on the other hand you have the
4:44 more classical approaches towards
4:45 artificial intelligence and machine
4:47 learning where people really get into
4:49 the mathematics and the statistics
4:51 giving you road maps where you really
4:54 have to get theoretical first I'm a firm
4:57 believer of learning by doing reverse
4:59 engineering things that people have
5:01 already done putting in practice and
5:04 then trying to fill in the gaps now the
5:05 technical roadmap that I'm going to
5:09 provide to you will really focus on the
5:11 fundamentals that you need in order to
5:13 get started in either artificial
5:16 intelligence data science or anything in
5:18 between like I've said I've worked in
5:20 all of these fields over the past 10
5:23 years and I've really identified the
5:26 core techniques workflows and tools that
5:28 you need in order to get started
5:30 regardless of what you want to do so
5:31 this will work for you if you just want
5:33 to build applications with large
5:35 language models and Lang chain for
5:37 example but it will also work if you
5:40 aspire to become a data scientist or a
5:43 machine learning engineer now the actual
5:46 first step that I would focus on on my
5:48 AI Journey would be to set up my work
5:51 environment now what does this mean so
5:54 python is the go-to language that we
5:56 have to learn if you want to get started
5:58 in AI or in data science but the thing is
6:00 is
6:02 Titan if you start to follow these
6:04 tutorials online videos training videos
6:06 courses even you can quite quickly
6:08 understand Python and how it works
6:10 because it's one of the easiest
6:14 languages to get started with but I
6:15 found in my personal Journey that
6:18 there's this initial bump where you see
6:20 things online and you see people run
6:22 some code but then you are missing some
6:24 information on okay but how do I now
6:27 actually do this on my laptop on my computer
6:28 computer
6:31 and I would really focus on this first
6:33 setting up an environment on your laptop
6:35 on your computer where you have an
6:37 application a program and a python
6:40 installation that you are confident with
6:43 and now I have a specific approach that
6:46 I take over here within fias code and a
6:49 lot of people seem to like that so make
6:50 sure to check that out in the resources
6:53 but this really is step one they're
6:55 getting accustomed with that and that
6:57 brings us then to step two which is
7:00 actually getting started with python
7:02 it's like I said the most important
7:05 language this is going to be your tool
7:06 that you're going to build these
7:08 applications in now if you're new to
7:10 programming at all I would first focus
7:13 on the fundamentals of programming which
7:15 I will have resources to but then
7:17 quickly transition into learning the
7:19 basics of python and then specifically
7:23 some libraries that are very useful for
7:26 AI and data science in particular so
7:28 these would be for example the numpy AI
7:31 Library the pandas library and the matte
7:33 plus lib library now these are all
7:35 libraries that you can use to do data
7:37 manipulation data cleaning creating
7:39 visualizations this is really your
7:41 starting point for starting to work with
7:44 data because in the end all AI
7:47 applications all AI tools are created
7:50 from data with data so being able to
7:52 work with data and turn raw and
7:56 unstructured data into information into
7:57 valuable insights that you can actually
8:00 do something with is is really at the
8:02 core of of artificial intelligence and
8:05 now step three would be to learn the
8:08 very basics of git and GitHub now why is
8:11 that some would argue that that would be
8:13 a little bit more advanced and it's not
8:15 required in the beginning but what I've
8:16 found especially with artificial
8:18 intelligence and also the video
8:21 tutorials that I make is that a lot of
8:23 examples online people will make that
8:26 code available via GitHub but you have
8:28 to understand kind of at the very base
8:30 sick how these tools work because that
8:33 allows you to easily copy and clone is
8:35 what they call it tutorials that brings
8:38 us to step 4 which is working on
8:40 projects and building a portfolio and
8:43 for this it's convenient if you already
8:45 know how to use git so you can download
8:47 some projects download some code from
8:49 from other people and then try to
8:51 reverse engineer it to me that really is
8:54 the best way to to Learn Python to get
8:57 good to actually understand holistically
9:00 what a project looks like how people are
9:01 structuring their code and trying to run
9:04 it and then you don't understand what's
9:06 going on but then trying to reverse
9:08 engineer so it's really like beginning
9:11 with the end in mind and then trying to
9:13 change things and see how that affects
9:15 the different outcomes and this also
9:18 provides you with an opportunity to
9:21 explore what it is specifically that you
9:24 like about artificial intelligence all
9:25 the areas we've discussed computer
9:27 vision natural language processing
9:29 machine learning he here you really find
9:31 out okay these are all the kinds of
9:32 things that I can do and this is really
9:35 what I like to do and then as you're
9:37 working on these projects selecting them
9:39 picking them you there will be a lot of
9:40 gaps and and things you don't understand
9:43 and that would be a good point if you're
9:45 interested in that to find specific
9:48 pieces of information or courses to help
9:50 you with just that and now when it comes
9:53 to projects probably the best place to
9:55 start if you want to learn more about
9:57 data science and machine learning is
10:00 kaggle so kaggle is an excellent
10:03 resource that you can go through and
10:05 they host machine learning competitions
10:07 here so you can see all kinds of
10:09 requests and you can even win prizes so
10:11 this is one from Google and the cool
10:14 thing here is if you click on the actual
10:17 competition you can also actually have a
10:19 look at submissions that people have
10:21 made so here you can see an entire
10:23 notebook from someone
10:26 that is trying to solve this problem for
10:29 Google all with documentation and and
10:32 even the code so this is such an
10:34 excellent learning resources source that
10:36 you can go through like I said there are
10:40 plenty plenty of resources available on
10:42 here but if that's not for you machine
10:43 learning data science if you want to
10:46 just explore large language models in
10:48 open AI for example right now then I
10:50 recommend to check out my GitHub
10:53 repository on Lang chain experiments so
10:55 I also have videos on my YouTube channel
10:56 for that but here on the repository
10:58 that's why it's good that you at least
11:00 understand the basics of git and GitHub
11:02 so you can take this code know how to
11:04 work with it so here are some cool
11:06 examples of how you connect can create a
11:08 YouTube bot that can summarize a video
11:11 or even a slack bolt or a Ponders agent
11:12 that can ask questions and answer
11:15 questions about large data tables and
11:16 now if you're really serious about
11:18 learning artificial intelligence and
11:20 data science and another great resource
11:22 that you can check out is Project Pro
11:25 which I've recently discovered so
11:28 project Pro is a curated library of
11:30 verified and solved end-to-end project
11:32 Solutions in data science machine
11:35 learning and big data so overall this is
11:37 just an excellent resource with with so
11:40 much information and all the projects on
11:42 here that you can pick from all from the
11:45 various fields are all created by top
11:47 industry experts from leading tech
11:49 companies so what I really like about
11:52 this is first of all you have about 3
11:54 000 free recipes that like anyone can
11:55 check out but if you get to the
11:57 subscription and that is why it really
11:59 gets interesting you have access to 250
12:02 plus end-to-end projects so you can
12:04 really like go in here and see okay what
12:05 is it that you're working on so maybe
12:07 it's data science and you want to
12:09 specialize in machine learning and you
12:12 go in here you literally have all kinds
12:15 of projects and this is not only a great
12:17 resource for you to learn from because
12:19 you will have complete video
12:22 walkthroughs 24 7 support and you can
12:24 ask questions and and you can even
12:26 download all of the code so literally
12:27 the entire project will be made
12:30 available to you so it's a excellent
12:32 Learning Resource but also for me
12:34 personally working as a freelance data
12:36 scientist this can also like really help
12:38 me in my professional work that the
12:40 projects that I take on so for you that
12:43 could either be in your job or in future
12:45 jobs freelancing whatever you really
12:47 have a library that you can pick from
12:49 that can really give you that extra kind
12:51 of confidence you need for example to
12:53 take on a project now like I've said
12:56 really you see video instructions you
12:58 can go through everything and then also
13:00 download the code so this really is a
13:02 great resource that you can check out
13:03 and if you want to learn more about this
13:06 I will leave a link down in the
13:08 description and project Pro also has a
13:09 YouTube channel which you can subscribe
13:11 to if you want to stay in the loop learn
13:14 more on that and that brings us to step
13:16 five which is picking your
13:18 specialization and sharing your
13:21 knowledge so right now you understand
13:23 the fundamentals of python you have a
13:25 work environment and some some efficient
13:28 workflows that you can follow you also
13:30 have some project experience so now you
13:33 get a little bit more clarity of what it
13:35 is that you want to do within the world
13:37 of AI or data science or machine
13:39 learning so this would be the point
13:41 where you pick a focus area you
13:44 specialize you try to learn more and
13:45 also what I really would recommend and
13:48 what I would do is to start sharing your
13:50 knowledge so you could do this through a
13:51 personal blog you could do this through
13:53 writing articles on medium or towards
13:55 data science or you could even
13:57 potentially like I'm doing share your
13:59 your knowledge on YouTube and by doing
14:02 so you're not only contributing to the
14:04 collective knowledge on AI and data
14:07 science but it's also an essential
14:10 method for you to strengthen your own
14:12 learning because in doing so in
14:15 explaining Concepts that you're working
14:16 on that you're learning to to someone
14:19 else you really start to identify the
14:21 gaps within your understanding and this
14:24 again allows you to fill in those gaps
14:26 accordingly and really focus on some
14:29 specialized learning versus just going
14:31 through course after course after course
14:34 and then step six would be continue to
14:37 learn and upskill because now that you
14:39 have Clarity on your specialization and
14:40 kind of the direction that you want to
14:42 go and you also start to identify these
14:45 gaps within your own understanding
14:47 it might be time for you to for example
14:51 focus on math focus on statistics if you
14:53 want to become a better machine learning
14:56 engineer or a data scientist but if
14:57 you've decided to go with the large
14:59 language model and generative AI route
15:03 you might identify that you need some
15:05 software engineering skills actually
15:07 really start to understand how you can
15:09 work with with apis and create
15:12 applications and that's like I think the
15:14 main main message that I wanna want to
15:16 provide you with with regards to this
15:20 roadmap and and my Approach is that it's
15:23 everyone's journey is is unique and
15:24 depending on what you want to do with AI
15:27 there's a specialized learning path for
15:29 you specifically so my goal is to really
15:30 provide you with the tools and
15:33 techniques to quickly get going
15:36 get your hands dirty identify problems
15:39 work on projects and then fill in those
15:42 gaps and then finally step 7 would be to
15:45 monetize your skills now this could
15:47 either be through a job this could be
15:49 through freelancing or this could be
15:52 through building a product but where the
15:55 real Learning Happens is is when there
15:57 really is some pressure onto it so it's
15:59 all fun and games when you're trying to
16:01 explore this within your free time
16:03 following some courses following some
16:06 tutorials but when it's your boss or
16:08 when it's a client that's that's
16:10 breathing down your neck for the
16:13 deadline that is where you really push
16:15 yourself that is where you really get
16:18 creative get resourceful and try to
16:21 absorb and learn as much information as
16:25 possible to just get the job done and
16:27 that's it those are the seven steps that
16:29 I would take today if I had to start
16:32 over completely from scratch on my AI
16:35 Journey and now another bonus tip that I
16:38 can provide you which will make a great
16:40 difference is surround yourself with
16:43 like-minded individuals who are on the
16:45 same track the same path as you who
16:47 share the same interest where you can
16:49 bounce ideas off where you can share the
16:52 latest news and tips with and in order
16:55 to facilitate that for you as well I
16:57 have an exciting announcement because
17:01 today I will officially be releasing my
17:04 free group called Data alchemy that I
17:07 would like you all to invite you this
17:09 will be a group where I not only share
17:12 the complete and entire roadmap that I
17:14 just shared with you with all the links
17:16 resources tools it will also be a hub
17:19 your go-to place to navigate the world
17:21 of data science and artificial
17:23 intelligence and everything that's going
17:26 on and happening right now within this
17:29 rapidly changing field so if you're
17:31 serious about learning artificial
17:32 intelligence and data science and you
17:35 also also want access to not only this
17:37 entire roadmap but additional courses
17:40 and resources then make sure to check
17:42 out the first link in the pinned comment
17:45 below this video and then I look forward
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