7 Mistakes Beginner ML Students Make Every Year

In this video, I share 7 common mistakes beginner ML students make every year! I myself made some of these and have personally seen many others doing them as well. I hope this video can help you catch yourself making any of these mistakes so that you can avoid them!
Enjoy 💛

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================== Timestamps ================
00:00 - Intro
00:33 - Jumping straight to Neural Networks
02:32 - Ignoring Algorithms and Data structures
03:57 - Ignoring the Fundamental Math
05:41 - Having a Ridgid Mindset
07:24 - Overthinking
09:01 - Playing Single-Player Mode
10:33 - Doing too many Projects
=============================================

#ai #machinelearning #mistakes

[borismeinardus]

🚀 There is so much more to explore in ML. Feel free to grab my FREE cheat sheet of different ML domains and open challenges:

[jt]

Math isn't hard. You just have to find a teacher who explains it well. The more logical something is, the less likely it is for the experts to have above average levels of communication. You're probably not dumb, you just haven't found someone who explains it in the way your brain works. Math is a universal truth. Teaching is hard since people see the world and absorb information differently.

[alien]

Amazing points to remember, Thank you so much, Boris

[MsAln]

Thank you so much Boris. You give some of the best ML advice I ever seen on YT. As a quick joke, I am low-key happy about the LLM hype, because the other fields of ML might become slightly more accessible. 😅

[rugvedpalodkar]

Hi, I made one of those mistakes i.e. directly start deep learning. Now I want to dive into the classic Machine learning algorithms. Thank you for this video

[pinakinchoudhary]

Your content is really underrated... hope you keep on making amazing videos like these ! 😊

[carsongutierrez]

This is so great for me! Thank you ML Guru bro!

[BenjaminGlidden]

Thank you for making this video, super underrated channel.

[mramar]

Mal wieder ein sehr gelungenes Video 💪🏼

[imaanislam]

Man, I have been listening to many ML guys. but i dont think they tell the things the right way, I was figuring out the next steps in ML journey, and you made it clear for me, . Sooo happy. blessings

[aniqatiq]

I guess recreating research paper is most essential and crutial. You are absolutely right. Been missing this technique.

[barbaragendron]

This is 100% accurate. Because of LLMs being my research topic I went through many papers recently, therefore I can bring another reason why you should not restrict your study to LLMs: there is a need (hence an excitement) for other approches to enhance LLMs abilities. Few-shot learning, meta-learning, multimodal setup, knowledge graphs to name a few. So the more you explore the field, the better prepared you will be for the future of LLMs :)

[asatorftw]

Hey Boris! I appreciate your channel and I would love to see some actual building of ML projects! I feel that YouTube is slowly turning into a "give generic advice" platform, mostly because those kind of videos are just so easy to make. I don't want to criticize what videos you make, all I'm saying is that I crave videos with practical substance =D

[ashleygirgis]

I was just about to scrap the stanford ML specialization to jump to LLMs. Thanks so much I needed to hear this!

[Weak-AI]

Thank you Boris. What tool do you use to make your animated diagrams?

[novantha]

I've been aware of the fundamentals of machine learning for around a decade now, but I just recently got into it and took the plunge. I have no PHD, or higher education that would suggest knowledge of machine learning, but I feel I've learned a lot pouring over research papers and applying them in code over the last six months. Strictly speaking, odds are that I shouldn't be able to get into an engineering position this way, but stranger things have happened.

I'm not really sure how to explain it, but in the last two months or so that I've been properly implementing things, particularly research papers without existing code implementations, I've really felt that it's all started to make sense.

Who knows, maybe I'll luck out.

[EdFormer]

I would argue that a bit of iteration between classical ML and DL can keep things exciting. I did my PhD on a sequence generation problem that was pretty much entirely dealt with using HMMs, which became the first ML algo I studied. The second was GANs, which at the time was considered the most challenging DL method to work with (possibly still is!), and I ultimately got my PhD for showing they performed better in the problem I was considering than the existing approaches. Since then, I've gone back and forth between DL and classical ML algorithms and have done postdocs on projects involving various applications of ML and now teach the subject at Master's level - from K-NN to the most obscure SSL computer vision methods. I think I would have lost interest with just learning classical ML before I got to DL - learning about them in parallel helped keep it exciting AND helped me link things together. Completely agree with all your other points though! Fundamental maths and computer science concepts are everything and knowledge about advancements in LLMs is nothing - if you know higher order Markov chains then LLMs are better equipped to work on them than anyone who doesn't.

[nickjames]

Amazing video!!! I'm a recent Electrical Engineer BS grad but I really want to get into this! I will be starting my MS in CS this Fall but want to hyper focus on the pre-requisite math, data structures, and algorithms that will give me a solid foundation. I have some experience from my undergrad but not enough to feel super confident. Does anyone have any recommendations on online resources to get fully caught up on these core foundational classes. Particularly a class that is teaching these topics with the intention of being applied to AI/ML?

[fakeavangchhia]

I have alraedy learn basic programming and matrix algebra, will it be okay to start from NLP?

[diegofilipe]

If I may say, I'd add another mistake. As you said people tend to create just a notebook and put all your code inside. Generally, this code has the data as input and a model or metrics as outputs. I suggest makingake this code as a pipeline. Think about how you could make this reproducible as a manufacturing mat. Modularize your code and break it into small pieces independently.