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I believe! Currently, I am pursuing my master degree in Information Technology while working as a part-time Chef, yes 'Chef'. I realized that time management is an excellent and indispensable skills and it will prove while you're try to manage your life. I also become more interested in AI and I decided to relearn math to achieve my goal which is become a software developer. I thank you for this great video insight.

Would love to hear your experience below.👇If you need a good strategy to learn the basic math and get better at coding, check this out: ruclips.net/video/pyxb2Lt-KDI/видео.html

8:07 I guess that has to do with the different distance metrics they both relate to. Euclidean distance is the shortest distance (usually whats taught in school) whereas the Manhattan distance (also called taxi cab metric) finds the distance in a grid-like path with sharp corners, often see it used while working with maps.

Yes Yes YES!!! Very interested in seeing the mathematics involved with machine Learning, deep leaning & Artificial Intelligence in general

Thank you for explaining that to a T. Yeah I’m actually picking up textbooks so I can learn the math. At 43, I think in about 1 or 2 years of studying the math, I should be ready to apply the other easier part.

I took a maths for ML course..through Coursera or EdX....

Superb lecture,lucid explanation, please keep posting videos of further lectures, Thanks a ton

helps!! thank you!

Your handwriting is way better than mine, keep up the good work!

No it won't make ai easy or hard. I am ai engineer. For 5 years i have never calculated any sigmoid value by my own or did matrix multiplication to get the convolution of a input metrix. Just learn what concept is being used n someone have already written the entire code for u. I am very bad at maths but i do AI model development and deployment. U can skip lots of complex algebra calculus statistics n u will still be fine when it comes to doing AI

Do it!

yeah the problem i have is i find the branches of math for machine learning (linear algebra, statistics etc.) not as fun as the other branches (geometry, number theory, even topology)

This channel will grow i’m so sure

Great video ❤

Again thanks a lot for the series i hope you continue no matter what

I don't understand the point you're trying to make here. Most research papers use Torch. Torch abstracts away a lot of the math and makes AI extremely easy even if you don't know all the math. Maybe you meant to say, "math is important and you should learn it while learning ML"? Because if so, then I'd agree. But to say math makes ML "extremely easy" is silly. Torch makes ML easy. Math makes ML work. And math is hard. But I always support more math videos, so go ahead.

Back propagation is a Partial Differential equation.

Heya, loving this series. It made me realize that I've only learnt how to solve these maths in my previous semesters but failed to understand why I was doing them or why I needed them. Thank you so much, these videos are really great <3

Are the four videos enough for all basic python?

Hi, is the textbook Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville, good enough, or is it necessary to go beyond ? I think there is a whole part dedicated to ML maths

I'm in a masters program for working professionals and I don't think they go into the math enough.

The failure of society and general education system.

For sure!! im looking forward to the videos

keep going sir im always check on your math playlist

the road map was nice but, better to learn data cleaning and preprocessing before algorithms. because these algorithms need clean data

If you're a software engineer, just wanted to share an absolute gem resource I found during this initial phase of learning ML/AI. (SWE 7+ years exp, just started collab with a ML startup) Usually people advise to read Machine Learning by Goodfellow, but having personally close to no initial math skills, the material became real hard real quick. Instead, I found this book called Math for Programmers, which explains and visualize math(and machine learning) with python code examples. All the code from the book is available publicly on GitHub, so you can follow along each chapter and run the code to see the data yourself. Learning math interactively and seeing the results of my changes in real time is huge for me, I can be curious, experiment and even rewrite things if they do not make sense to me(until I reach some roadblock or inefficiency, that makes me understand even better why the initial mathematical solution is the way it is). More over, given that you're already working with Python code, you can ask advice to LLMs and get even a more personalized learning experience, plus you're already working with the toolset you'll actually use during ML work.

Math to me is like some people saying put an orange in a strawberry and a banana comes out. I m like waaaat?

This is like saying, moving furniture is really easy if you workout. Ya its easy because you already did the hard work.

Yes , linear algebra and probability

Good stuff, just starting with machine learning and want to learn math behind the concepts

I ditched a lot of courses because teach algorithms only, but not the math

College and university degree programs in AI/ML require you to take math courses. It's only online that you see courses in these topics without math.

I feel like the actual rant is supposed to be around the fact that you actually don't need the math for it :D. I think many people that are trying to train models heavily rely on already built systems and libraries. They take validated datasets from online resources and tweak a few parameters. Then they continue with "Try and Error” for some time until they reach a point to be satisfied with. I don't know how many of them are really interested in analyzing own datasets in depth like what a high bias results in or a high variance. What it means, if grad. desc. oscillates with huge amplitudes. The libraries are enablers, which is kinda great I guess. At the end they won't wrap their mind arounds the actual architecture, since they cant really understand what's happening under the hood but build extensions for the architecture thus founding systems of use. At the end im pretty happy how I started my journey into this field by keeping distance to acutall NNs for several months, focusing on math. The math is the real fun regarding AI, NNs etc. For me that's the point where logic and creativity meets to make things happen.

Sir Please post the videos regularly We are looking forward Thankyou

We want to know how a non cs background student can learn the aiandml and get a job what projects to focus on and roadmap to land a job

I feel like this video was made for someone like me.... I consider myself a very capable software engineer, I have 6 years experience in data engineering and I have dreams of moving into ML but I have no math background and that has always dissuaded me. I don't want to be the guy who is just calling models without understanding any of the math behind it. Please do make some videos on how someone like me might approach learning math to move into an ML role.

Let me know what concepts you want to learn next! 👇And THANK YOU all for supporting the channel as always! :)

I wish you develop a systematic mathematics course for ML or AI

Hey ChemCoder! I appreciate this video of yours. In today’s hype-driven world, people dive into Machine Learning without understanding the math, which is so crucial. There's a funny observation I’ve made about my university teachers-they routinely split the dataset into a 7:3 ratio for training and testing, then fit the model and check the accuracy. They don’t seem to understand the mathematics behind Machine Learning. I agree it can be challenging to balance learning the core concepts of Machine Learning with the underlying math, but it’s essential. Otherwise, the foundation would be weak

should I learn physics before learning to drive a car? you can do the ML without the math, you just need to understand the concepts if you want to use ML in your day to day projects. forget the math and you can always come back to, don't let it get in the way to learn and use ML.

Hi sir. I have a very interesting question that I have been asking myself for a long time: Is coding dead because of AI according to the CEO of NVIDA? I think that you could give me the right answer for this question because you are already in this field. I am pretty sure that lots of your subcriber also very interested in this question and all of us will appreciate you if you could help us find out the right answer. Thanks. LOVE YOU AND YOUR CHANNEL

Thank you so much! I’ve been lately graduated in chemical processes so I really needed this !

Great going ChemCoder... All the points that you are considering in each of your videos are bang on ...empathizing a beginner....with all sorts doubts that a novice can have...and the poasible solutions to those...You seem to be a good teacher...Looking forward for your upcoming contents....All the best !!!

I learned the maths in a business school!

In the age of AI, is it necessary to learn machine learning?

Subscribed, excited for part 2! Thanks so much for making these amazing videos and explaining it so well

put us on big dawg

Bro thank you. No one even talks about this unless you go and look for it.