Become better at machine learning in 5 min/ week 👉🏻 borismeinardus.substack.com/

i watch his videos and end up with the thought , 'bruh , i still dont know shit'

I've been binge watching all your videos. Learned a lot tbh. Thank you for clearing these concepts so easily.

I was literally confused about these roles before I even watched this video. Thank you for making this video and helping me get clarity on each role and their specific responsibilities.

When a dude has this accent you know he is good at ML

Thanks for the explanations man, i was indeed confused about these roles

Pretty clear video, well done. Additional roles like MLOps Engineer and AI Engineer are also emerging

By far the best video on differences between various ML profiles....kudos!!!

Very good video, and your last points about it being easy for software engineers to move into ML. Anyone who has spent years building systems in many industries will be able to generalize the new data from ML. Researchers and developers need each other, and I rarely find someone who is great at both.

This is actually great.! Keep it going

This clears a lot of things up. Thank you for taking the time to make this!

This was a much needed video.Thanks!

much awaited! thanks!

Hey Boris, I hope to find some help here. I arrived in Australia to start my master thesis. It is about optimizing prices parameters in a manufacturing environment. Where I have some machines for laying up carbon fiber (Multilayer-2D) and another machine with curing/press process, which adds resin to the fiber in order to harden it. Basically I gotta somehow check out the input data and see the correlation. How can I do it? What would you do? The quality is not really measured numerically or binary. I just have some weird data sheets of the machines. I feel overwhelmed. Is there any machine learning algorithm you would advise? The aim is to actually see correlation, check out the critical parameters for the quality, then do some experiments with setting these critical parameters up, in order to validate the analytics part.

Thank you for the clear explanation! This is very helpful to me !

Hi. I have seen a lot of your videos lately and they are very helpful. Do you think a Nvidia GPU is necessary for a beginner in ML if I plan to train my models on cloud with Google Colab? Also Should I prefer Intel or AMD? Thx

Really informative descriptions👍

thank you Boris, this differentiation between the different roles have been very interesting and insightful, I definitely understand them better. I have a question tho if I may, you said that it's easier for a software engineer to transition into ML. However, talking about my case, I'm usually better at maths, and I'm just starting to learn python, sql and the relevant languages for the domain of data science. For someone who wants to work as an ML engineer, do I need years of experience as a software engineer first? or is it possible to break directly into the field with enough personal projects for example? I know this is a difficult and vast question, but if you have some tips that would be awesome.

Hey man, love your videos and explanations. :)

What a great explanation! Loved it, keep up the great work:)

This is the best video I've found on the topic! Can definitely confirm that the details are pretty accurate based on my experience in DE, DS and MLE. Can you make a video on applied scientist role, eg how to develop problem solving skills for such a role, example projects to build such as kaggle competitions, etc? Thanks!

Hey Boris, please where can I find your paper on road network and predicting traffic flow? I am doing research on something similar, it might help 🙏🏻

Amazing video, thank you!

Hey Boris! Love the vid. As someone who doing their undergrad and was confused with the difference between these roles, really appreciate the clear explanation! Also would love to know how your path looked like getting that applied scientist role! Definitely would help me recognise what to do, since I’m not in a very prestigious university but have been doing most of my learning on yt or coursera😅😅

Hi Boris! Thanks for the great content, as always. I have always had confusion regarding the differences between various roles in ML and AI; your video clears up many of these. I wanted to know your opinion about something related to this topic. How much does one's PhD major matter in the case of getting a job as a research scientist and/or applied scientist? I am very eager to know this because, at least for now, my dream is to work as an applied scientist or research scientist in the future. However, I am probably going to start my PhD in the College of Pharmacy in the coming fall. My PhD area will be, in general, in the field of healthcare AI for pharmaceutical outcomes and policy determination. I am confused regarding whether I should accept the position or not. Or should I try again next year for a PhD since I don't have any more offers in the ML/AI field this year? Competition in the ML/AI field is increasing tremendously! Feeling hopeless from time to time, to be honest!

AMMMMAAAAAAZING EXPLANATION !!!!!!!!!!!!!!!!!!!!!! TRULY AMAZING!

Really excellent explanation. Thank you!

Really useful info, well presented, thanks

Simply awesome content! Thank you!

Hello, could you help me with a question I have. I know that there are master's degrees that are 100 focused on automatic learning, but do you think that a master's degree in pure mathematics along with small complementary courses could help me ?

Very well put. I usually have to explain this myself but now i have a content to point other people to 😂

Doing a masters in AI to transition into tech. I’m gonna watch this fully later but it seems like it might be better for me to simply to being a software dev instead of a specialized role like ML engineer or research engineer? Let's say I rack up experiences in data engineering, would I still be eligible for a career path like a dev?

So in addition to ml/dl skills & knowledge we must also know data engineering?

Extremely helpful thank you

Thanks, very useful!

You communicate your points and ideas very well.

To be a machine learning specialist, do I need to be skilled at building machine learning models at a low level from scratch (not using existing machine learning packages)?

So, if i am recruited as a ml engineer in a company, will it be 100% a coding or like programmer role?

Loved your video sir , sir in future video we want you to teach us about these things that you have told us

Very helpful video, helped clear up the chaos in the head when it comes to job titles. Sometimes it feels like there’s too many. Just a language suggestion though: maybe better to use “they” when referring to ML engineer rather than “he”. Due to understandable reasons

This is very useful, thank you!

but does a ml engineer also source and clean data and builds the model and then deploys it? Its confusing because sometimes the ml engineer is described as the one who should only deploy the model and the data scientist builds the model. and sometimes the ml engineer works also with data and does the complete machine learning pipeline. and the data scientist does not only build a ml model. so confusing.

Your my best motivation to learn ML thanks for uploading videos ❤❤.

As an ML Engineer, I feel like we're a slightly worse version of each of the other roles, which can actually be really valuable for a company. I've done ML Ops (deploying LLMs into prod), Data Engineering (ETL pipelines, data lakehousing), Data Science (building bespoke models from scratch), Software Engineering (building the system to run everything on) all under the same job role with the same company.

Please do a video on how much math you use as machine learning researcher

now there is a question for now. If MLE job roles are speciliazed form of SWE, What is the definition of job the SWE :D?

Which is the highest paying role amongst this? Is it research scientist in big tech/startups?

What are good sources for research papers? Are there free resources or do most need a subscription?

It would be great if you could make a detailed roadmap vid on how to become an applied scientist and a research engineer

Stabil Boris!

this video reads like a resume pre prepared for when the ML bubble pops

amazing video brother

I have a genuine question. There is Data Engineer, ML Engineer and AI Engineer. Do you see in future any of these merging together? Should we try to be an ML Engineer or directly jump to being an AI engineer?

Ive been thinking of going the data engineering route once I graduate. Is that possible with a BA/BS in CS?

Bro i want to see how you edit your videos, they are really cool

Amazing video!

Should I study a pure computer science degree or get a specialization in Data science for my degree?

ML Engineering: DataOps, back-end, etc. Data Engineering: Database, data extraction, feature extraction, storage, etc.

Bro please make a video about ML mathematic 🥲😭.Love from Bangladesh ✨

I really wish you had a discord group as well😊

Thanks!

Vro Is AI and ML are same or different if yes then explain me in short

Ok so basically ML engineer deploys ML algorithms on applications for users based on the custome'r's data in businesses ,I get it😂😂

"the goal of a data scientist is to generate business insights". not really. ive worked as a data scientist on pure research problems, as well as on production-level code for shipping out solutions.

Pretty much my job... all of them 😂

We are waiting you to launch your machine learning bootcamp and we will pay.

No ones talking how bros nick is so bigg and looks like f1 driver😂

Guys, if you do ML, and you are surprised by a KZfaq video. That means that you have research culture whatsoever. Your degree is worthless if you can’t read the literature.

Damn according to this video I’m far knowledgeable then I give myself credit for

Thank you so much for the explanation! One small thing though: All your example mini videos depict male engineers/researchers and you also always speak about "he" as in a definitely male person. I know that coming from German you are used to a generic masculine (even though even in German this is highly debatable), but in English it's way easier to generalize. Maybe it would allow your viewers to have a less biased image in their head when they think about an ML scientist etc.

🤖🧠

I feel like data preprocessing and analysing will soon be done by models instead of humans writing algorithms.

2:12 I’m completely confused. Did three or four takes and retakes of sections. Don’t know what he’s talking about? All mumbo jumbo like. Stopped watching. Won’t be coming back. Sorry.

This kid doesnt know squat

Faker science

Awesome video! You did a great job.