How I'd Learn AI in 2023 (if I could start over)

If you want to

learn

about artificial intelligence, then this video is for you. I'll give you a complete roadmap that I would follow if I were to

start

over today on my journey into AI and now, for context, I've

start

ed studying AI again. in 2013, 10 years ago and for the last few years I have been working as an independent data scientist helping my clients with various end-to-end data science and artificial intelligence solutions and applications. I also share all this knowledge and my journey on this YouTube Channel which today has more than 25,000 subscribers and at the end of this video I will also provide you with a completely free resource where you can follow all these steps to complete the roadmap , including training videos and instructions, so be sure to Stay there and now, before we dive into the seven steps I would take today to go from beginner to monetizing my data and AI skills, it's important to provide some context on what What is currently happening with the AI ​​hype because I see a lot of new people entering the field and for good reason because the size of the AI ​​market is expected to grow up to 20 volts by the year 2030, reaching almost 2 trillion US dollars , so it is really one of the best opportunities.

I would say that right now we need to get in because we are still in the early stages of this AI revolution and also with the release of these open AI pre-trained models it is also now easier than ever to get into the field, but that being said, it is also That's where a lot of misunderstandings and wrong expectations come from because I see a lot of people online and on YouTube explaining how you can quickly start, for example, your own AI automation agency and while there are great tools out there. are already online, like push and stack Ai and flowwise, which I also made a video about where you can quickly create prototypes and simple Bots and you can even be a little 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 create applications that companies can count on and develop, then you really have to understand the coding part, the technical part, so that's where our starting point should be for you. and for your learning path, figure out if I just want to learn how to use these Loco no-code tools that are already available or I really want to learn artificial intelligence and that being said, I think there's also a general misunderstanding about what AI actually is. This is because AIS is a very broad umbrella term and it's also nothing new, it's been around since the 1950s, but right now with the hype of GPT chat and open AI models, people think that AI is really that, if we look at what artificial intelligence really is.

I've said a really broad umbrella term with several subfields, for example within artificial intelligence, which is explained here as programs with the ability to learn and reason like humans, machine learning, then we have deep learning, which is another subset that focuses 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 you need to ask yourself is: do you want to? being a coder and now there is no right or wrong answer here, there are many opportunities right now and also in the future for both Pathways, both for local NOCO tools and for building custom applications, but you just have to keep in mind the pros and cons of both. from the sides and not to be totally clear, this roadmap is for people who really want to learn AI with a deep understanding, really learn the technical side of things and now, if you've decided that's not for you, of course , it's totally fine.

Like I said, there's nothing right or wrong, but if you still want to do things with AI, I recommend starting by checking out both presses, how I've set up or stacked AI, which are great resources or you can watch my video. at flowwise here on YouTube, where I show you how you can get started with a local NOCO 2 and completely free, but if you decide that you want to join the Dark Side and become a coder, then let's proceed with the next steps. It's quite different from anything else you can find online and now why? And what I typically see online is that you have two ends of the spectrum, basically where, on the one hand, you have people talking about these low-code, no-code tools. really getting into the specific theoretical part and then on the other hand there are the more classic approaches towards artificial intelligence and machine learning, where people really get into the mathematics and statistics, giving you roadmaps where you really have to be theoretical first.

I'm a big believer in learning by reverse engineering things that people have already put into practice and then trying to fill in the gaps. Now, the technical roadmap that I'm going to give you is really going to focus on the fundamentals that you need. To get started in AI data science or anything in between, like I said, I've worked in all of these fields for the last 10 years and I've really identified the core techniques, workflows, and tools you need to get started. To get started regardless of what you want to do, this will work for you if you just want to build applications with large language models and Lang strings for example, but it will also work if you aspire to become a data scientist or machine learning engineer.

Now, the first real step I would focus on in my AI journey would be setting up my work environment. Now what does this mean? Python is the go-to language that we have to learn if we want to get started in AI or data science, but the thing is Titan, if you start following these tutorials, online videos, training videos, courses, you can even 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 problem where you see things online and you see people running some code, but then you're missing information, okay, but how can I do this now on my laptop?

I would really focus on this first on 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 here within the fias code and a lot of people seem to like it so be sure to check it out in the resources, but this is really the first step that you're getting used to and that brings us to step two, which is actually getting started with Python. It's like I said, the most important language, this will be your tool.

You are going to create these applications now if you are new to programming. I would first focus on the fundamentals of programming which I will have resources for, but then I would quickly move on to learning the basics of Python and then specifically some libraries that are very useful for AI and data science in particular, so these 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 manipulate data, clean data, and create visualizations. starting point to start working with data because, in the end, all AI applications, all AI tools, are built from data with data, so being able to work with data and convert raw and unstructured data into valuable information that you can actually do something with is really the core of artificial intelligence and now step three would be to learn the basics of git and GitHub.

Why some would say that would be a little bit more advanced and not necessary at first, but what I have discovered, especially with artificial intelligence and also with the video tutorials that I do, is that there are many examples online in the that people make that code available through GitHub, but you have to understand from the beginning how these tools work because that allows you to easily copy and clone is what they call it tutorials that take us to step 4, which is working on projects and create a portfolio and for this it is convenient if you already know how to use git so you can download some projects, download some code from other people and then try to reverse engineer it for me, that is really the best way to learn Python to really understand comprehensively what a project looks like, how people structure their code and try to run it and then you don't. understand what's going on but then try to reverse engineer it, so it's really like starting with the end in mind and then trying to change things and see how that affects different outcomes and this also gives you the opportunity to explore what is specifically what you like artificial intelligence, all the areas that we've discussed, computer vision, natural language processing, machine learning, here you really find out, okay, these are all the types of things that I can do and this It's really what I like to do and then, like you.

If you're working on these projects, selecting them, there will be a lot of gaps and things you don't understand, and that would be a good point if you're interested in finding specific 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 a great resource to check out and they host machine learning competitions here so you can You can see all kinds of submissions and you can even win prizes so this is one from Google and the cool thing here is that if you click on the actual competition you can also see the submissions that people have made so here you can see a complete notebook of someone who is trying to solve this problem for Google, all with documentation and even the code, so this is a great source of learning resources that you can check out, like I said, there are a lot of resources available here, but if that's not for you, machine learning data science, if you just want to explore large language models in open AI, for example, right now, I recommend you check out my GitHub repo on Lang chain experiments, so I also have videos on my YouTube channel for that, but here in the repository, so it's good that you at least understand the basics of git and GitHub so that you can take this code and know how to work with it, so here you have some interesting examples of how to connect, you can create a YouTube bot that can summarize a video or even a loose bolt or a Ponders agent that can ask questions and answer questions about big data tables and now if you really want to learn artificial intelligence and data science, another great resource that you can check out is Project Pro, which I have recently discovered, so Project Pro is a curated library of verified and resolved end-to-end project solutions in data science, machine learning and big data , so overall this is a great resource with so much information and all the projects here that you can choose from all the different fields, all created by top industry experts from top tech companies, so What I really like about this is, first of all, it has about 3000 free recipes that, like anyone, you can check out, but if you go to the subscription and That's why it gets really interesting: you have access to more than 250 projects end-to-end, so you can actually come in here and see what you're working on, so maybe it's data science and you want to specialize. machine learning and you come in here, you literally have all kinds of projects and this is not only a great resource for you to learn because you will have full video tutorials, 24/7 support and you will be able to ask questions and you can even download all . the code, so literally the entire project will be available to you, so it's a great learning resource, but also for me, who personally work as a freelance data scientist, this can also help me a lot in my professional work, as that the projects that I undertake for you, that

could

be in your work or future freelance jobs, whatever you really have a library that you can choose from that can really give you that extra kind of confidence that you need, for example, to take on a project now, like I've actually said. you see video instructions, you can go through everything and then also download the code, so this is really a great resource that you can check out and if you want to learn more about this, I will leave a link in the description and the Pro project also has a channel of YouTube that you can subscribe to if you want to stay informed, learn more about that and that brings us to step five, which is choosing your specialization and sharing yourknowledge so that you now understand the fundamentals of Python and have a working environment. and some efficient workflows you can follow.

You also have some project experience, so now you have a little more clarity about what you want to do within the world of AI, data science or machine learning. This would be the point. When you choose an area of ​​focus that you specialize in, you try to learn more and also what I would really recommend and what I would do is start sharing your knowledge so you can do it through a personal blog. You can do this by writing articles in a medium or towards data science or you

could

even say that I am sharing your knowledge on YouTube and by doing so you are not only contributing to the collective knowledge about AI and data science, but it is also an essential method to strengthen your own. learn because by doing so, by explaining the concepts that you are working on and learning to another person, you will really start to identify the gaps within your understanding and this will again allow you to fill those gaps accordingly and really focus on specialized learning instead of just following course after course after course and then step six would be to continue to learn and improve because now you get clear about your specialization and the kind of direction you want to take and 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 data scientist, but if you have decided to go down the big language model and generative AI route, you may identify that you need some software engineering skills, in fact, start to understand how you can work with API and create applications, and that's it. I think the main message I want to give you regarding this roadmap and my approach is that everyone's journey is unique and depending on what you want to do with AI, there is a specialized learning path for you specifically.

So my goal is to really give you the tools and techniques to get started quickly, get your hands dirty, identify the problems, work on the projects and then fill those gaps and finally step 7 would be to monetize your skills. Now, this could be through a job, this could be through freelancing or it could be through creating a product, but where the real learning happens is when there's actually some pressure on it, so It's all fun and games when you're trying to explore this in your free time by following some courses that follow some tutorials, but when it's your boss or when it's a client breathing down your neck during the deadline, that's where you really push yourself, it's where you really get creative, be resourceful and try to absorb and learn as much information as possible. just do the work and that's it, those are the seven steps I would take today if I had to start completely from scratch on my AI journey and now another additional tip that I can give you that will make a big difference is the environment. yourself with like-minded people who are on the same path as you who share the same interest where you can exchange ideas where you can share the latest news and advice and to make that easier for you too, I have an exciting announcement because today I will officially launch my group free game called Data Alchemy that I would like everyone to invite you to.

This will be a group where I will not only share the full roadmap that I just shared with you with all the links. resource tools, it will also be a reference center for navigating the world of data science and artificial intelligence and everything that is happening right now within this rapidly changing field, so if you are serious about learning artificial intelligence and data science and you also want to access not only this complete roadmap but also additional courses and resources, then make sure to check out the first link in the comment pinned below this video and then I hope to see you in the foreign group.