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