Machine Learning is EASY with these Math skills (and how to learn them)

[@panizzutti]

This “trick” of you is really useful but a lot of college teachers don’t do that, after i started doing that more and thinking more about everything math and algorithms became so much simpler

[@nAna36628]

desculpa, eu nao pude entender seu comentario em inglês. acho que vou ter que ver o video inteiro

[@raduionescu1003]

statquest is also very good for understanding specific concepts

[@JoseTavares-xu8pc]

Double bam

[@sylvr12]

BAAAAM

[@returncode0000]

Awesome content, I really appreciate all your videos, helped me a lot because I just was asking myself what I need to learn on math to get really good in ML, I currently search for clinical datsets and try to solve problems and create models with sklearn. Thanks and keep up the good work!

[@InfiniteCodes_]

Glad to help! Thanks for the nice comment and support!

[@amanshaikh04]

Underrated Fr 🙌...... such quality content needs more attention.

[@ujjwalbansal5649]

Point you are trying to make via Linear regression example at 5:08 which I first look Linear regression equation its look like exactly the same as straight line equation from geometry class which I learn in high school. And rest all other equations that are part of this topic of linear regression was going over my head. So, I sat on night with whatever silly doubt coming to my mind I give it to chat GPT and validating my understanding. After 3hrs of chatting and lot of digging with Chat GPT I get each and every part of this equation cleared. Like why I was comparing this model equation with geometry taking me to wrong direction, what is sense of doing square at RSS step, etc. Even I saved my whole chat with chat GPT in word document..... I am still in the process of learning but one thing which I totally agree is that strong "Mathematical Intuition" will help us more in understanding the things better. Just shared my little experience.... Thanks for this video.

[@Bond-zj2ku]

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

[@NoDrizzy630]

Well I’m glad I researched this field before i started my undergrad in data analytics and will do my MSc in data science. Longer route and math used to give me anxiety but it’s a must have, also a competitive edge against my boot camp competitors.

[@JayamNagomi]

I like your way of explaining formula your one example made me search your channel for all over the youtube Please please start a series explaining all ml formula with statistics 🙏 ❤ Hope you reach more and untill its not gonna struggle searching you

[@mohitbokade2691]

Please make the video on Bias and Variance and the biase variance tradeoff

[@AnnaRushi]

Your channel is amazing this exactly what i needed. Thks man 😃

[@find2hard]

Ritvikmath is also a good channel for explaining statistic and ml concepts.

[@anuvabkumarrit4669]

Would very much like you to cover, bias, varience

[@2ru2pacFan]

This was very insightful! Thank you.

[@BederikStorm]

You mentioned only z-score. Depending on actual distribution and the way unknown parameters are linked to standard deviation, you might need t-distribution or chi-squared.

[@har81181981]

5:50 That's a great video. I'm really curious as to how understanding various math concepts are used like random variables, probability distributions, matrix rank,etc. Would love to see a follow-up video on the math and use cases

[@bongkem2723]

basics and fundamentals are king in almost any domain, and even here in ML, math/bias-variance is still king despite the insanely fast evolution of AI.

[@tayloresch1909]

I would love a video about bias, variance, an the bias variance trade off

[@acrane3496]

3:41 so basically be a math major. Or more specifically, approach formulas like a math major or mathematician

[@Sadikshah099]

very helpful video 🙂💕

[@ujjwalbansal5649]

And ya before realizing that I need to develop some foundation in probability theory concept and shifting my self to book "First course in Probability by Sheldon Ross". I saw few starting lectures of Statistical Learning playlist on Stanford learning channel. OMG author of "Elements of statistical learning " teaching themselves .... One thing I tell you guys those guys know their stuff .... let me say it again they know what they are talking about.... I highly recommend if you know little bit about linear regression just saw first few lecture... You got amaze..

[@djsid75]

Thank you❤ keep uploading video like this, easy explanation

[@abdulwahabkhan1086]

3Blue1Brown is gem ❤

[@YashNaik-j8o]

Hey, I found your videos helpfull can you make video on multidimensional arrays?

[@manfredbogner9799]

Sehr gut

[@FahadKinqBtdbBs]

I have heard dsa is also important for data science what do you think?If you think it is can you provide a few resources because i really liked your explanation and resources because it really saved me from burnout...

[@InfiniteCodes_]

Thanks for the comment! I'm assuming you mean data structures and algorithms? While I think its an important CS concept and definitely cant hurt to know, if you are close to burnout I think you can skip it for now :) Knowing how to work with numpy arrays and pandas dataframes will be more important to you on a practical level. I still think it's good to know once you are further from a burnout because it will give you a good intuition for efficiency in Computer science. I am actually working on a script for a video that should help you a lot, won't be the next video but the one after that (on how to learn data science efficiently and not waste time)

[@FahadKinqBtdbBs]

@@InfiniteCodes_ thanks i watched your recent video too before this comment and will watch that one too thanks for guidance :)

[@shafiq_ramli]

...I'm going to stick with web dev 😂

[@InfiniteCodes_]

🤣

[@data_eng_tuts]

Good for you 😂

[@milan0023]

I am thinking this too 😢😂, will also try this too

[@EshwarBhore]

Man you made me laugh after many days of depression 😂😂

[@abdulbasitbello2381]

Great video

[@panizzutti]

Such a good video

[@NeTRunneR077]

I don't see any point in memorizing ML algorithms.

[@mymoviemania1]

Bootcamps will loss customers if they say that you will need some math.

[@ChemCoder404]

I made a similar video on my channel. But this one’s better! Hopefully we can collaborate someday. 😅

[@amiladissanayake341]

There are three stages of truly understanding the math behind machine learning: 1. The Unfamiliar Stage At first, the math can feel cryptic and disconnected. It’s hard to find the intuitive meaning, but persistence is key- don’t give up. Keep pushing through! 2. The Familiar Stage After some struggles, things start to click. You begin to grasp what each formula represents because you’ve caught onto the ‘vibe’ of machine learning. 3. The Mastery Stage Now, you don’t just understand the intuition, you play with the formulas. You have an instinct for them, a sense that lets you not only understand but also anticipate new insights. True understanding means not just knowing but sensing it deeply. Give time for your neuroplastisity.

[@WensonChen]

Are you German?

[@cuicuidev]

This is all very basic stuff any student could learn within half a year and I have a really hard time believing this isn't taught at bootcamps, especially since it is where I am from

[@nokts3823]

I believe there is a difference between being familiar with the concepts and truly understanding them intuitively and being able to apply them in practice. For instance, any high school kid knows the chain rule of calculus, but I don't think they will be able to backpropagate though any layer in a NN by hand without a good deal of exposure and practice first.The concepts might seem simple enough, but leveraging them in the real world is usually not straight forward.

[@mymoviemania1]

Most of the people in that field are bad at maths and not bright. Frameworks are what made them Data Scientists / ML Engineers etc.