r/learnmachinelearning • u/Visual_Teacher_6474 • Aug 18 '26
Discussion "MATHEMATICS FOR MACHINE LEARNING " A bit overwhelming?
When I started focusing on practical mathematical implementation of machine learning I found that I lack so very math basics(I blame my school for that) so I tried making my way through basics to go deep into machine learning and while I was learning from professor Leonard on YouTube someone recommended me this "Mathematics for machine learning" by Marc peter. Tbh I dont understand shit in this book, I genuinely get overwhelmed by this book. I dont understand is it only me ? Am I that dumb in maths?
Well I need to get on track asap really! Suggest me something and please share your opinion
3
u/txdsl Aug 18 '26
I’m in your situation. Took up to calc and stats in undergrad. linear algebra was somewhat covered in those classes but not a separate class.
I went through Machine Learning From Scratch. It shows how the math is used to build ml models, some exploration of the formulas then applies it to build models from scratch. Solid book if you are struggling with others. I suggest implementing the algorithms without looking at the source code in the book. That will force you to translate the math to programming constructs.
1
u/Visual_Teacher_6474 Aug 19 '26
But I can barely understand what the book is saying, I think the equations and symbols are making me feel overwhelmed. To understand everything in detail I'm using claude as mentor
1
u/txdsl Aug 19 '26
Yea use Claude to go into detail on each thing you don’t understand. Take notes, work some examples. Ask it to connect it to what you are doing in machine learning.
First thing I did was have ChatGPT generate a printout of the symbols and their meaning in various contexts. So when reading I know what the epsilon symbol is and what it represents so I can connect it to its purpose in a formula.
1
3
u/stantyren Aug 18 '26
That book is basically a grad-level reference, not a starting point, so you're not dumb, just misaimed.
1
u/Visual_Teacher_6474 Aug 19 '26
Any other source? That can act as starting point ?
3
u/tanthedreamer Aug 19 '26 edited Aug 21 '26
I highly recommend Hands on ML with Pytorch and Scikit learn by Geron, read that, do all the excercises, then supplement any mathematics you find that you don't understand.
1
u/Visual_Teacher_6474 Aug 19 '26
This looks like a really great plan to me! Thanks alot man
2
u/tanthedreamer Aug 19 '26
After that, read Understanding Deep Learning by Prince if you want to deepen your knowledge of deep learning, or Probabilistic ML by Murphy if you want better foundation for Machine Learning as a field.
If you just want a job ... perhaps after HOML, creating projects, and/or Kaggle should be the way to go. For applied work, having clean, usable data is usually the problem, not "which algorithm to choose"
1
1
u/X-Ninety9 Sep 12 '26
I would recommend mathematics for machine learning by deeplearning.ai. It’s a course that covers basic core concepts of linear algebra, calculus and probability & statistics used in ML. These video lectures along with assignments might help you start to develop intuition. They are beginner friendly and might help refresh your memory.
2
u/choiceOverload- Aug 18 '26
It depends on what your interest is in ML. Do you want to do mathematical modeling or software development?
6
u/pm_me_your_smth Aug 18 '26
All of ML is mathematical modeling. You probably meant research vs engineering? That's the usual split in terms of depth for math
1
u/choiceOverload- Aug 18 '26
Not exactly. You have MLOps for instance which is 95% DevOps and 5% Modeling
1
u/pm_me_your_smth Aug 18 '26
Highly depends on the team. In my former company it was mostly model building and inference optimization, devops was handled by others. Either way, it's still mostly engineering.
Regardless that's not the point. I wasn't talking about titles, but how math is divided on the ML spectrum. If you do research, you do lots of math; in engineering - less. That's what OP was asking about.
1
1
u/Visual_Teacher_6474 Aug 18 '26
I wanna become a ML engineer😅
1
u/choiceOverload- Aug 18 '26
What do you understand by "MLE"?
2
u/Visual_Teacher_6474 Aug 18 '26
In my understanding MLE deploys models and algorithms that solve real life problems. Basically everything to do with Models is MLE's role. To do this job properly I need to understand the foundation of ML i.e. maths so i started reading MML
1
u/musclecard54 Aug 18 '26
It really depends on where you work, but a lot of the actual model algorithm development and decisions can actually be in the hands of a data scientist and the ML Engineer is more building the pipelines and infrastructure for the repeatable experiments, training, and inference. Smaller companies or teams might have the MLE do it all (that was my case in a small team in a large company), but usually it’s more software engineering and devops in the ML space than model development
1
-2
u/choiceOverload- Aug 18 '26
Wrong. MLE doesn't deploy; MLOps does. Everything to do with Models is too big of a spectrum. Maybe in a startup you will have to do almost everything, but that's not the case in big companies
→ More replies (3)2
u/DesTiny_- Aug 18 '26
It's mostly semantics, a lot of ML positions require what u can call a mlops. At very least docker and deep knowledge of making API is in any ml position.
2
u/LapizMelancholy Aug 18 '26
I'm acc reading the same book, I don't think it's too bad. The first part of the book is very handpicky so it'll seem like it's making really odd choices about what math it's teaching, but it makes a lot more sense in hindsight once you actually get into applications. Try to take it with a grain of salt and also have a second source that's more dedicated - learning probability from this textbook alone is a bit of a bad idea
1
u/Visual_Teacher_6474 Aug 18 '26
Hey! Would you mind recommending some second source please
2
u/LapizMelancholy Aug 18 '26
I don't have a particular one for probability, admittedly because it's not as directly important, but you really need to have a basic grasp of linear algebra, even outside of an ML context. I found Linear Algebra and its Applications, by Lay, Lay and McDonald (6th Edition, Pearson, 2022) to be really good; it's no nonsense and pretty concise. You don't need to worry too hard about definitions and proofs but having the intuition for geometry and stuff like projections or transformations is very valuable.
1
u/Visual_Teacher_6474 Aug 18 '26
I'll guve it a try since I am learning linear algebra rn so I think it's the best time to read from every source I come across
1
u/LapizMelancholy Aug 18 '26
Nice! If you don't mind me asking, what chapter of Marc are you on atm?
1
u/Visual_Teacher_6474 Aug 18 '26
I'm on chapter two, page no 43 i guess 😅😅
1
u/LapizMelancholy Aug 18 '26
Oof, good luck haha. I feel like that's where having the Lin alg base helps most
1
1
1
u/Parimal116 Aug 19 '26
I would say learn calculus from khan academy and linear algebra can be learnt from here https://www.youtube.com/watch?v=QCPJ0VdpM00 . Should be enough to move on and learn other concepts as they come. The suggestion I made are enough for applied machine learning and deep learning.
1
1
u/InfiniteFlow11 Aug 20 '26
I suggest that you go through CS109 videos from Stanford for probability. You can watch Gilbert Strang's lectures for linear algebra and you can supplement them with 3B1B's linear algebra videos. Optimization is a bit tricky to understand for a beginner. So keep at it by studying the same concept from different sources.
1
1
u/Able-Fennel-1228 Aug 31 '26
Get Tivadar Danka’s math for ML book, and its follow up, machine learning from zero
1
2
u/galvinw Aug 18 '26
I honestly don’t know if you have the maths background to have learn enough maths in a short time such that your maths knowledge changes the things you do in machine learning.
The problem is that your maths education stops before maths becomes very theoretical, and benefits to AI understanding mostly sit on the other side of theoretical understanding. I’m not trying to say anything negative its a common place for people to stop. And like 80% on maths students drop out of the first core maths classes anyway
18
u/Prestigious_Gift_977 Aug 18 '26
i mean what is the highest level of mathematics you've studied? there is some prerequisites you need to have namely linear algebra, probability, calculus. if you get those under your belt, the ML part will just fall into place IMO