So I get a lot of questions on how to learn machine learning and become an ML engineer or data scientist. I will soon start my dream job as an ML researcher, having been working towards this goal for the past 6 years and have learned a lot

about what to do and what not to do, at least in my opinion. And I've also seen a lot of advice from other people online that I think don't put that much thought into what they say. So in this video, I thought I'd share my honest advice on

how to actually learn machine learning. This is one of the most important pieces of advice I want to give because more and more people just ignore it. Let's be real, many of you will probably have seen ChatGPT and the other generative AI tools like image generation models like Midjourney or

DALL-E. And I get it, it is really super cool, but directly wanting to jump to generative AI is only really a good idea if you simply want to build a product that calls, let's say, the OpenAI API. And there are amazing online courses that

focus only on generative AI, but if you really want to become an ML engineer or researcher or something like that, that won't be enough. You need to learn the basics, and by basics I here specifically mean the classical ML models like linear regression, support vector machines, and

random forest. And classical ML know-how, such as what precision, recall, F1, confusion matrices, R squared, and so on are. You see, in the most practical sense, you will simply need that knowledge for interviews, but what I think is a really important indicator for skill is when

doing ML small talk with real ML engineers you might casually talk about these techniques and if you don't know them people will know you are not really that educated yet at least I know the old school ML is not the fancy stuff you see today but it definitely is core

knowledge a real ML engineer needs to have and is still used more often than you think so make sure to always have as much fun as possible but definitely don't skip these fun fundamentals you've had enough machine learning courses I know this might sound weird now but please let me explain what

I really mean by that being able to understand machine learning has some prerequisites like math and programming The logical next step is to watch some ML courses for example those by Andrew Ng on Coursera in those courses you learn the basics of ML and deep learning

which I mentioned in my previous point and get some practical coding experience so what now is the question what is the next course you should watch there are so many well honestly at this point I don't think you should focus on specific courses you should start to work on real

projects as early as possible machine learning is a very new and in most parts very empirical science you have learned the basics and at that point there is no clear path from A to B it's all exploration you will most likely feel lost that nothing makes sense that you

are wasting time and not progressing in the most efficient way possible but believe me you are doing just fine this is normal ML is still a really young field with so many separate developments there's a huge space of ML knowledge and you will be learning

different topics at random, you might learn about language modeling, which is somewhere in the top left corner of the ML knowledge space, and then reinforcement learning, which is somewhere in the bottom right corner. And there is no connection yet, but over time, if you just keep going and going, you

will see all the connections, more and more. Of course, you can never be an expert in all of them, but by simply staying curious, working on projects that involve these different techniques or domains, and perhaps watching introductory lectures on those topics, you will get a good, high-level

understanding of what is possible with each technique and what isn't, and you'll get a feeling for what you enjoy doing. But most importantly, you will get more practical experience with every new project. You will realize how bad you were in the previous ones and get better

and better. This process is very important, so don't waste time looking for the perfect next best course for too long after the basics. I honestly recommend for a more project-based learning approach because you are a software engineer. Pretty much all beginner ML students have a wrong idea of what doing

ML or working in ML as an ML engineer really means. Even ML researchers need to have strong software engineering skills. This took me a while to understand as well. Greg Brockman, a co-founder of OpenAI, said it's easier to teach people ML skills than software engineering. Another

great illustration of what the impact of software engineering compared to actual ML code and knowledge is. This visualization by Google showing how much the explicit ML code is compared to everything else that is part of an ML project. Again, ML knowledge is definitely mandatory, but as I mentioned in my

Previous point, don't obsess too much over ML theory after the basics. Try to build stuff. Don't think of these projects as a project to look cool on your resume, but as a project to learn. I.E. to actually improve your skills. And after a few such iterations, your

projects will naturally get better and better, more complex and interesting, and more impressive on your resume. And make sure to somehow show your work, for example in the form of a blog post or a working project website with a demo. All the debugging you will need to do, all the different data processing and

evaluation pipelines you will build, all the data visualizations you will create, the efficiency improvements and so on will be a large part of your project. And those parts will be equally as important as the few lines of actual Python code you will write, if not more so. Don't think

all this time spent coding and debugging is wasted. It is very, very important. This one is a scary one. There are thousands of ML papers published per year, wait, wait, no, per month. And there are always new libraries that claim to be better than others. How can one keep up with all the speed of

progress and information? Well, you need to realize that there is a lot of noise. It really isn't about learning all the newest libraries. Sure, PyTorch is cool, but by the love of God, especially as a beginner, don't get overwhelmed by such new libraries. You need to know a bit of

pandas, which almost everyone is still using. And if you will need to work with PyTorch at your new job, well, you will very easily adapt. I'm not hating on PyTorch by the way. I'm just saying you don't need to instantly jump to the

newest stuff and ignore the more important things. I mean, for my new job I will most likely need to work with Jax instead of PyTorch. I've never used Jax before and my interviewers knew that, but they know that I will be able to adapt

to the library. The basic libraries are enough and it even really isn't that important whether you started with TensorFlow, PyTorch, or Jax. It also really isn't about having read every new paper. It's more about recognizing trends and techniques that are being used more

and more and might be useful for your work. For example, especially for an embedded engineer, efficiency techniques are a big thing. For instance, at a certain point it will likely be important to notice the release of something like flash attention and understand what it is, or something like

KV caching or grouped query attention or something that currently is all the rage, like retrieval augmented generation, or for short, RAG. And if you haven't noticed, all these things don't really have much to do with new ML techniques, but those are engineering techniques. The amazing thing is that all this stuff is

so new that learning it will literally mean you are one of a fairly small group of people who are familiar with this technology. I know it might not feel like it because it might seem so popular, but think about another software industry like web development. Effectively, all the skills

you can learn there have been learned by many people who have started with web development perhaps 10 years ago and are now 10 years ahead of you. And since RAG and all these techniques are only 2 years old or so, you will be able to

catch up to the best much quicker than I said. Keep in mind the point I mentioned before with respect to projects: you need to actually get hands-on practice and work on projects to learn the skills and then be able to prove that you have the skills by showing your

projects, but all this it takes time. This is the most important point, period. So many other expert influencers try to sell a "become a Gen AI expert in only one month" product. What does "expert" even mean? The point is that simply won't work, at least not without a significant amount of

prior software engineering experience and a good understanding of basic ML. All of the points I mentioned before won't happen over the course of a few weeks, of course. How long it takes for you specifically to learn machine learning to a level where you are actually really

useful to an ML team simply depends on the level with which you begin this journey. Andrej Karpathy is a big believer in the 10,000-hour rule, and I am a big fan of it as well. If you invest 10,000 hours

into some skill, you will become very good at it. So don't expect to take one course, do one project where you train your first model, and then be ready for a real-world job. You need to work on project after project after project. Read into some literature whenever you

encounter something new, and repeat that pretty much forever. I know this might sound harsh, but I definitely don't want to discourage you. Rather the opposite: I want to encourage you to really learn machine learning, enjoy the learning process, and become a great machine learning engineer because I am certain

that if you enjoy it, invest the time and push through the bad times, you will become great at it and it is simply so fun and worth it on so many levels. I mean, for me it took six years of going to college, working as a part-time student

researcher in ML for over 2 and a half years, working on countless projects and failing a lot to now actually get my dream job as an ML researcher, which I still can't really believe. So if you want to really see what I went through

in all these six years, you might want to watch this video next. Bye-bye.

My Honest Advice to Beginner ML Students for 2025

👨🏻‍🎓 I like to train Neural Networks and understand intelligence
👨🏻‍💻 I talk about how to learn Machine Learning, how to get a job, and life as an ML researcher

Boris Meinardus

Importance of Foundational Machine Learning Knowledge

Many questions on learning machine learning and becoming an ML engineer/data scientist.

Advice on how to learn machine learning, focusing on often-ignored basics.

Warning against directly jumping into generative AI without foundational knowledge.

Importance of learning classical ML models (linear regression, SVM, random forest).

Necessity of understanding concepts like precision, recall, F1 score, confusion matrices, etc.

Project-Based Learning and Practical Experience

Understanding prerequisites like math and programming before starting ML courses.

Shifting focus from courses to real-world projects after learning basics.

Embracing the exploratory nature of ML and accepting the feeling of being lost.

Learning different ML topics at random and connecting them over time through experience.

Recommendation for a project-based learning approach, emphasizing practical experience.

Importance of Software Engineering Skills in Machine Learning

Many beginner ML students have a misconception about the role of software engineering in ML.

Emphasis on strong software engineering skills, even for ML researchers.

Visualization showing the significant portion of non-ML code in ML projects.

Focusing on building projects to improve skills rather than just impressing recruiters.

Navigating the Rapid Pace of ML Development

Addressing the challenge of keeping up with the rapid advancements in ML.

Focusing on fundamental libraries like pandas rather than getting overwhelmed by new libraries.

Adaptability to new libraries is more important than mastering every new tool.

Recognizing trends and techniques rather than reading every new paper.

Importance of understanding engineering techniques like flash attention and RAG.

Refuting the idea of quickly becoming a Gen AI expert without significant prior experience.

Time required to reach a useful level depends on the starting point.

Emphasis on the 10,000-hour rule for skill mastery.

Continuous learning through projects, reading, and repetition.

Encouragement to enjoy the learning process and persevere through challenges.