7 Mistakes Beginner ML Students Make Every Year 

🚀 There is so much more to explore in ML. Feel free to grab my FREE cheat sheet of different ML domains and open challenges: borismeinardus.substack.com/p/a-list-of-different-ml-domains

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.

Amazing points to remember, Thank you so much, Boris

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. 😅

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

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

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

Thank you for making this video, super underrated channel.

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

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

Very insightful. Thanks.

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

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 :)

Simply superb

Hey Boris! I appreciate your channel and I would love to see some actual building of ML projects! I feel that RU-vid 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

Mal wieder ein sehr gelungenes Video 💪🏼

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.

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.

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.

Great piece of advice

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?

I'm still watching just want to say you have one beautiful and kind soul I bove you so much you're great, Boris

Great video and I feel you are underrated. Thanks for existing on RU-vid. Could you tell the average salary and the salary range in the ML field in Germany?

Actualy when someone use the Term AI you knows he is highly incompetent or is working in the marketing department. 😅 Good video, like in all disciplines start from the foundation otherwise your growing potential and flexibility is limited very fast.

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

Good to remember that different programming languages have different cultures. I have been learning Julia and that has helped me a lot because I have been able to get help from Discord channels and forums. Also that culture steers you to certain path, i.e, Julia community often solves problems with a certain way.

thanks for advice , i am programmer starting to learn ml instead directly llm ... thanks for advice 😊

Your videos help me in decision making. I worked on few supervised learning projects as an Engineer and realised that i can't be top 1% engineer. I'm looking for AI PM roles where i can seemlessly discuss with ML Engineers without having to worry about bug fixes or days of uncertainty. May I know what challenges do you face when working with product managers ?

Thanks, Boris. I found your video very insightful. However, I have another question. Is it important to secure an internship or industry-related work experience in machine learning? I'm a final year student and most of my experience so far has revolved around research activities at the university. This includes participating in an undergraduate research program, working as a research assistant, and doing a few of personal ML projects. I would really appreciate your insights on this matter.

We need separate video about learning math the right way.

One last question, do you recommend to do ML crash course by Google before Andrew Ng's course. And a humble request , please make begginer friendly projects on ML as hands on project base learning really helps to understand the topic.Btw thanks for helping the community with your valuable knowledge 😊

Hi Boris, I’m focusing on computer vision can you suggest any courses or learning materials that focuses on computer vision. I want to create a model that detects if something got rotated, flipped, etc.

Hey I have completed the math course for machine learning provided by DeeplearningAi, do I need to deep dive more into, or it's enough to start and to build the foundation

My understanding is that there are no entry level MLE jobs. As a baseline you are expected to have senior level experience in Software Design and System Design on top of all the ML theory, methodolgy and tools. The likley path is to either be a SDE or Data Engineer first then transion into ML after you have enough experince working with data at scale and desiging systems at scale.

Should I study Design and Analysis of Algorithms then? I like the book “The Art of Computer Programming” by Donald Knuth, I’m hoping to start doing some problems with it when I get into uni

LLM’s are like the DOS operating system, it will be left in the dust.

excellent video. However if you are a developer and want to apply existing models to whatever you are doing, the best way to learn is doing. If you are a researcher then indeed....

Hi Boris! Thank you for your awesome contents. I really want to have a chat with you regarding directions about my ML career. But in the call booking link, there is only the option of paying with paypal. There is no option for payment with cards. Unfortunately, paypal isn’t allowed in my country. Can you provide any alternatives?

Mistake--not watching RU-vid videos by "'The data janitor"' Mistake--Either go top down or bottom up or sideways( and if your primary concern is money , don't come in ML...not that it is wrong... it's just that you're not going to make it) Mistake-- Believing (ok he has mentioned in the video,, as I am typing this lol..I guess mistake 5) Edit: This comment is for Indian viewers. Like most of the jobs are out of India, and in even in IITs, nits and iiits there is nothing serious going on ( research/ industry). Now you might land up a job simply answering how linear regression works or you might not get a job even after understanding everything in the paper "'attention is all you need"'. There's no framework and luck plays a major role.

🔥

Is google's machine learning crash course enough to get started? Im planning on doing Andrew Ng's course next

Hi Boris. Where do you think I should see research papers from? Most of them are so hard to digest.

Hey why don't you start a ML course , that would be really helpful 🙂 , I know it's time consuming , you can just guide us using others course

Strong in fundamental math, ok in coding still not getting any job 😢😢

Can you please make a vid on maths for ml, all maths in detail with if possible free sources on internet (Books and vids)

I am looking for good tutorial to learn basic ML to Advance ML with mathematics

I start my Computer Science Degree with a major in ML at UQ this year- any tips?

Hey what do you think about the application of machine learning in the cybersecurity domain?

TRUST ME YOU WORTH MADE

Also I am a good maths student and have currently completed the ml specialization course by Andrew ng where would you recommend I go from here?

How to find people for accountibility or to work with?😊

I have a bachelor in Computer Science and one year of experience in software engineering. Do you think getting a masters in Computer Science will make it easier to land a ML job?

Full stack web developer vs data engineer more job opening more package future growth

s sound is too loud

Hi boris -- Give me some good resources for learning machine learning and ai in reply

Totally agree with this video; I've had many students who hated ML because their professors taught many maths... Also, in case anyone would like to understand how simple is to write the code for any Machine Learning model from Scikit-Learn library: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-fEBxFWuth4Y.html

Please suggest some good resources for learning python

Hi, is there someone starting like me and what to learn together? Please leave a reply, people from all the countries are encouraged to join me and make a new friend and learn together... I'm from INDIA

Learn math and code it in Python

hallo, I saw you and your sister in the cinema watching The Boy and the Heron, i feel that we have a connection, we can be really good together. Please answer me, i love you...

This video is not making any sense to me. I am proponent of the "10000 hours rule". There is no perfect path to learn AI. Try the path which interests you the most. Andrew Ng suggests that an AI student should try to read and understand and deply AI research paper in various ways. Simply, there exists any foolproof path. There is no one path.

I read your medium article this video came into my feeds on RU-vid by "RU-vid recommendation " nice article and video :) I recommend don't jump to hype ! what you think