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This was super helpful. Should make it compulsory for all introductory ML courses. Sometimes a little hard to keep up, but that’s probably more to do with my more limited cognitive abilities!

Amazing!!!

Excellent knowledge!, Please keep on, this skill will to be expanded to other deeper as a baby try to grab as natural learning, guy!. GOOD E.P🎉

Hey would it be possible to contact you for some Questions or even an Interview? Me and a fellow student are working on a study for a guide for AI implementation in the Maintenance field. We would really appreciate your input on some topics regarding the AI field. Best wishes from Germany!

Hey would it be possible to contact you for some Questions or even an Interview? Me and a fellow student are working on a study for a guide for AI implementation in the Maintenance field. We would really appreciate your input on some topics regarding the AI field. Best wishes from Germany!

Hooooo It is crazy for I'm preparing a course and dude. You are one of the best teacher of internet ! It is so crazy that you don't have more subs but trust me I am and I will share your chanel as much as i can !!!!

IMBECIL ESO LO HICE EN PRIMARIA

Incredibly well made and informative, this channel is a goldmine. Would love to have a video covering more advanced concepts like activation functions, transformers, autoencoders, etc.

Sir you are the son of GOD. Thank you for your nobility.

Think of a healthy big athletic guy who has been forced to feed only on cucumbers 🥒 for an entire year ... How happy would he be after being offered a thick juicy steak 🥩, along with spaghetti 🍝, marinara sauce, and meatballs 😋 ... Multiply his happiness a 1000 times, this is one millionth of how happy I am with your videos ❤

YOUR VIDEO APPEARS TO MY HOME BECAUSE IM STUDYING TO ML , ML OF RUclips SHOWED ME, RIGHT?

Is there any resource which has a concise summary of the foundational ML concepts and Algorithms that one can use for revision.

@3:36 where is this image from? :-)

nice video but not giving credits for used images is not cool

Great vid , but I think you missed one of the most important notes here , which is the final step , landing a job.. Idk if it seems obvious or not , but im really wondering , after all of that , your skills and foundation is job-ready , but how would you proceed , what certificates/diplomas in ML/AI are most renowned online ... ? I looked up on reddit and other articles , people are saying getting a ML job is nearly impossible without a traditional cs degree , which I found weird, the articles are several years old though

Great video. No bullshit practical application, not selling you stupid courses for $500 or whatever the F.

I was going back and forth with so many ideas. But this it. I now know what I want to do. Thank you very much.

what an amazing and understandable videos hope you will continue this series. i understand things very well!

How are you supposed to implement a ML algorithm from scratch, is this a realistic task?

good video :)

1:01 barista 💀

Best short yet informative story about Data Science Journey ever to be found! 👏🏼👏🏼👏🏼👏🏼👏🏼 thanks!

Isn't Double Descent already proved the Bias-Variance Trade-off irrelevant?

bro starting off with image of person wrist deep into the backside of animal lmao

From someone that has (maybe) the majority of the math knowledge that you talked about, I can't emphasize on the importance of the python libraries that you talked about Pandas, Numpy and sklearn, we sometimes forget how important they are especially if you come from a theory based background. Also, If you have some knowledge gaps (especially in the hypothesis tests in stats) you'll have a big trouble trying to make good predictions and/or insights. Also, I want to make a point on the part when you talked about calculus, I think it's important to study not only derivatives of single variable function, but also for multivariable ones, because it's important , for example to understand from where the estimators formulas in multiple linear regression came from. Good advice.

Please bro give me advice because I'm not cs student 😢

Nice vid! What are the hardest things about being DS? I mean soft skills area I'm currently working as a BI analyst, and I struggle a lot with confidence, earning trust to my dashboards among colleagues, I see DS route as one way of my futher career development but all these communications just drains me out

You explain the main goal of minimising rss. Could you please make videos on other equations from statistics and linear algebra

Yeah, man. You know what? You're some sort of Didactics Super Sayan. Thanks for the video. Instant subscribe.

But the term “Variance” in the Bias-Variance tradeoff DOES represent variance! It is the variance of the model’s predictions, Var(f~(x)). As the training data X is a random variable, so is the learned model and the predictions it produces f~(X). The rough intepretation of the variance term would be that the model changes a lot based on the training data

Can you give me the file for this presentation?

When it comes to ML, I often get lost and here it is a game changer. Thank you for your guidance. I have saved a lot of time.

for those watching and reading - I never finished HS - I didnt attend Uni. I have been a paid programmer for almost 25 years now. I never had impostor syndrome, because I never had all the accolades you guys get before starting. Dont believe it - its WEAK thinking. Push those obtrusive thoughts out of your head and just do it. Its the difference between "doers" and "talkers" - "winners" and "losers" I work in AI/ML now - still no degree. I learn the maths I need at each stage.

I usually watch these kind of videos (where the guy is yapping some roadmap or something) in 2x. For this guy I switched to 0.75x (< 1) in the first 2 minutes of the video.

Seriously most people or training school claim 3 or 6 months to be ML engineer. Thats almost impossible for those without a STEM degree at least.

3:59 this is sadly not true. Will you build programs fast? Yes. Will the programs be fast? ABso-fucking-lutely-not. You will need data science concepts, *a lot* to make python fast, you will need to dive into cython, and probably even binary code, and build libraries in a low lvl language, that suit your project, in order to make anything workable in python. Numpy is great and people use python because of tmodules like numpy, whicih is built in C and Fortran (wrapped in C and then into Python). So, yeah, don't keep your hopes too high for this tip.

This video is very informative. Short but very precise.

best video on this topic 💯

I strongly disagree on your screenshot with comically obvious and redundant comments as an example of good documentation. Maybe it was necessary 50 years ago, when languages and compilers were not that great, but today it's just like using a horse for transportation. Writing the same thing as the code, only in improper language like English, is bad. It's acceptable as training wheels, but after that it just creates extra confusion and debt. Having a comment is like crying "wolf!", if every line has them, nobody will pay attention to them. It also enables bad naming and structuring practices, since it gives a false sense of clarity. I'd say that if you have an urge to comment something, probably you should rename your variable or extract a function, not just write a comment saying that your variable myCatIsGreat is in fact just an index.

how can i get the full image from 4:30?

something confused my mind now, if every one has his special needs in learning, so why do we all learn the same thing in school and university?

14:05 as a developer my PR review would be to remove those comments. Function descriptions are fine. Comments on very complex logic is also good. That is just repeating the code.

6:03 isn’t the opposite? with k=1 we have underfitting and with k=1000 we have overfitting

overfitting in really big NNs is a more or less solved problem if you also have enough compute. For details read "Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets". All big models use this phenomenon.

step one: just start doing ml projects and learn in field like we are supposed to. seriously, if you wanna learn anything just go do it and stop being stuck in analysis paralysis.

That last part about building off tutorials is very real. I find that most of the youtube guides/tutorials are very basic and meant for beginners, but if you take something basic and implement your own ideas it can become a larger and larger project which helps a ton

You should always focus early on the fundamentals, regardless of which field are you studying, period

I learned all that and built projects to then discover than I need to to master cause am not coming from CS background 😶 but business. The video is awesome man but I think if u offer mentorship I should be interested to join u and try to work on a group. Am in process of enrolling into master of data science program so I am doing lots of extra miles at this point.

13:59 This is horrible! Documenting all lines of code means you create unreadible code :) Code should be self-explenatory and comments should help to understand main ideas, algorithm features and use cases and sometimes why it is written in this manner (optimization, special case, etc) :D

The 100th commenter, U are like the big bro who spoke the utmost truth . ❤🙇