Machine learning and AI is extremely easy if you learn the math: My rant.

You just started learning machine learning and AI but wonder why everyone insists on learning the math behind it? To complete projects in ML, you need to know mathematical concepts in AI and machine learning. In this video, we break down why understanding the mathematical foundations is not just important but essential for mastering machine learning. We'll explore why concepts like linear algebra, calculus, probability, and statistics is necessary to learn. Whether you're a beginner or an experienced practitioner, this video will help you appreciate the math that makes machine learning work, and how it can deepen your insights and boost your skills. Let's demystify the math and unlock your full potential in ML!

Stay tuned to stay ahead of the curve in the world of science!

#coding #ai #machinelearning #sciencemajors #datascience #python

Welcome to ChemCoder. As a computational chemist myself, I had to transition to AI and ML towards the end of my PhD program and I have to admit, it was a lot of learning, making mistakes and late nights. If you are a graduate student, freshman, sophomore or a senior in college and wondering if this field of chemistry and programming is for you, you ARE in the right place! We as chemists or as scientists in general have a very unique position to learn and integrate our coding skills to bring new technology and innovation to the world. The future depends on us and our ability to learn, stay motivated and persistent in this difficult field. Join our community as we strive to get better every day.

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[d.youtubr]

Necessary roadmap (books) for a noob:
1. Elementary algebra for school - Knight
2. Higher algebra - Knight
3. Calculus made easy - Silvannos
4. Problems in calculus of one variable - Maron
5. First course in Probability - Ross
6. Introductory statistics - Ross
7. Linear Algebra - Strang
8. Practical Statistics
9. Basic Multivariate calculus

[fireinthehole]

Hi, ML PhD here: learn the math. It might not be super fun but it will be worth it and it gives you tools you will use your entire career.

[Vishnu-nl]

Yes, please teach us machine learning with a focus on the mathematics behind it, starting with perceptrons and vectors. Comparing machine learning with mathematics will help us learn more quickly and effectively. I am eagerly waiting for your upcoming video.

[dominicellis]

Gaussian curves, logistic curves, langrange multipliers for gradient descent, basic group theory to understand backpropogation, lots of linear algebra for the weights and bias, and regression analysis to train your model. It’s all basic undergrad math.

[katalyststem]

Hi ChemCoder! I’m a doctor, specializing in diabetology, and I spend quite a bit of time analyzing patient data (good old Excel). I’m really excited to dive into your videos to refresh my math skills, especially as they relate to machine learning. Thanks for all your hard work—more power to you!

[samsonv]

Yes pls make a video with math concepts behind ML, thank you 🙏🏼

[edwardgrigoryan]

Thanks for this video. It provides validation for the path I have chosen for myself to eventually learn ML/Data Science. I have dedicated 1.5-2 years just to learn the math (clearly I'm not in a big hurry and this won't apply to everyone). I just finished reviewing precalc, and am going to move on to doing the entire calculus series and linear algebra, along with a major emphasis on stats and eventually the ML specific math that is needed to be highly competent in the field. Currently work as a product manager/data analyst for a startup, but want to gradually upskill to ML/Data Science over the course of a few years. It just didn't make sense to me to skip or rush through the math. Perhaps it helps that I actually enjoy the math quite a bit. Anyway, once again, thanks!

[LeFrog]

I'm actually teaching an introduction to supervised deep learning for undergrad programmers this year. It's supposed to be a maths course, so I've decided to deconstruct the pipeline and focus on simple examples, explaining the maths.

The first 3 or 4 lessons just focus on non-convex optimisation in one variable, gradient descent for a linear regression, with or without stochasticity, and the PyTorch framework using `backward` and an `Optimizer`. Then I'll introduce MLPs and transformers.

I've only had one lesson so far, but the students have been really receptive. Their comments and your video are encouraging me that I'm on the right track, thanks!

[basuutube]

i am interested in learning the math behind. will eagerly await for your math playlist

[fkgkskw]

yessss ! someone talking about this I thought I was the only one who noticed that no one teaches maths... make a playlist I beg you !

[joecavanagh]

I’d like to offer a suggestion: it may not be the best approach to dive straight into the required math when first exploring machine learning. For someone who has just developed an interest or curiosity in the field, I would recommend starting with the aspects that initially caught their attention and experimenting with those. This way, they can gradually build on that interest without being overwhelmed by the more challenging sub-skills, like math, which might feel discouraging at the beginning. My advice would be to first become proficient in the areas that excite you, and then gradually develop mastery in other essential areas, such as mathematics, as you progress.

[kirillholt]

I've learned math on my own time (had a background already) but what still escapes me is the motivation behind it. Why some machine learning algorithms are the way that they are.

[Camstraction]

I completely agree with you and I wish that there was more of an emphasis on the math in machine learning courses. It is very understated how important it is to understand the math behind the algorithms in order to apply machine learning models in the real world, where often the use cases are not cookie cutter like presented in courses. The only way to appropriately choose the right algorithm, loss function etc.. is to understand the math behind it. I mean even our evaluation metrics matter there are many limitations that have to be understood and it all lies within what the math actually tells us about our data and6 model performance.

I am a math major and enjoy pure math for its own sake but there is a real (and big) gap between the math and it’s implications in applied settings. It’s not an easy gap to bridge and often requires jumping back and forth (without good direction) between your specific application and the seemingly disconnected math. This is because the way math is taught doesn’t lend itself well to generalization in the real world by nature of how math is taught but also by how math is made.

I often find myself trying to find resources to bridge this gap and like you’ve mentioned, it’s hard to find any. It really would be a great help to myself and many others for people like yourself to help bridge that gap!

[reallynotpc]

When I started learning maths, machine learning hadn't yet been invented! But, yes, I did do some basic statistics and I think linear regression was part of my A-level syllabus. That's a good rant you have there! Happily, I am rather old and will never need ML to make a living.

[freeideas]

I understand a bit about matrix multiplication, but so does numpy. I understand a bit about how the weights and biases are used between the layers, but so does tensorflow. I understand a bit about using calculus derivatives to find the minimum loss, but so does keras. I make neural networks all the time, but I'm not sure how I would use any math directly.

[sarak]

Yes, we need the ML Math playlist please.

[ChanceMinus]

Yes, I am extremely interested in learning the math behind Machine learning. I am a Data Engineer transitioning to become a Data Scientist. Becoming more involved in Generative AI. Thank you.

[Endou]

I'm 100% interested in hearing more about your takes/maybe even some classes or book recomendatations for foundamentals of math for ML. Please keep it up

[Jennifer_]

I would be interested in learning the math behind ML!!!! I just found your channel! Thank you for sharing!

[MalamIbnMalam]

In my graduate school program for Masters of Computer Science they expected us to already have the mathematical foundation in Calculus, Linear Algebra and Statistics/Probability. So it was not reviewed. I had to review it on my own.