How I’d Learn AI in 2025 (if I could start over)

How I'd Learn AI in 2025 (if I could start over)



Here's the roadmap that I would follow to learn artificial intelligence (AI).
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⏱️ Timestamps
00:00 Introduction
00:34 Why learn AI?
01:28 Code vs. Low/No-code approach
02:27 Misunderstandings about AI
03:27 Ask yourself this question
04:19 What makes this approach different
05:42 Step 1: Set up your environment
06:54 Step 2: Learn Python and key libraries
08:02 Step 3: Learn Git and GitHub Basics
08:35 Step 4: Work on projects and portfolio
13:12 Step 5: Specialize and share knowledge
14:31 Step 6: Continue to learn and upskill
15:39 Step 7: Monetize your skills
16:53: What is Data Alchemy?

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👋🏻 About Me
Hey there! I'm Dave, an AI Engineer and the founder of Datalumina, where our mission is to facilitate entrepreneurial and technological proficiency in professionals and businesses. Through my videos here on this channel, my posts on LinkedIn, and courses on Skool, I share practical strategies and tools to navigate the complexities of data, artificial intelligence, and entrepreneurship.

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#ai #roadmap #datalumina

📌 Video Description
In this video, Dave shares a comprehensive and actionable roadmap for anyone looking to start their journey into the exciting world of artificial intelligence (AI) in 2024. Whether you're a complete beginner or someone looking to pivot your career towards AI, this video lays out a step-by-step guide that demystifies the process of learning AI from the ground up. Dave highlights the significance of AI in today's tech landscape and addresses common misconceptions that newcomers might have.

With a focus on practical learning, the video emphasizes the importance of choosing between a code-centric or a low/no-code approach, making AI accessible to a broader audience. Dave's unique approach involves asking a critical question that shapes the learning path, ensuring that viewers embark on a journey tailored to their goals and interests.

The roadmap detailed in the video covers essential steps such as setting up your learning environment, mastering Python and key libraries crucial for AI, understanding the basics of Git and GitHub, and the importance of working on projects to build a strong portfolio. Dave also talks about the importance of specialization and the continuous process of learning and upskilling in fields like generative AI, large language models, chatbots, and machine learning.

Furthermore, Dave shares insights on how to monetize your AI skills, turning your passion into a profession. The video concludes with an introduction to Data Alchemy, a concept that encapsulates the transformative power of AI knowledge.

For those eager to dive into the AI world, Dave offers a free roadmap accessible through the link provided in the video description. This invaluable resource serves as a compass for navigating the complexities of AI learning, making it an essential watch for anyone interested in artificial intelligence, machine learning, and related technologies.

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 foreign

#Learn #start

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