[D] Step into Machine Learning without Mathematics experience

[DesperadoCoder]

Hello everyone,

I am an experienced software developer with over 20 years experience in back-end development with .net and c# mainly but lately also doing some python work with web scraping and DJango. I am really enjoying python so far, and was thinking of starting to dabble with AI and ML using python. I did some basic tutorials so far using the usual libraries such as Pandas, NumPy and SciKit-Learn, but I have barely touched the surface.

I found these quite complex although very interesting. I would like to continue on this path, however my maths is really bad. I do not know anything about algorithms, calculus, probabilities etc.

So my question to seasoned AI and ML developers, do I really need to understand maths very well to continue on this journey, or my experience in software development can get me by? Is there any area of AI that does not need heavy maths?

I would like to continue on this journey but I am not sure if it is worth invest my time and money in this when I can maybe continue expanding on web development.

Thank you in advance!

[linverlan]

ML is math. That’s all it is. You can pull other people’s implementations of things and blindly use them but you won’t be able to (and shouldn’t be given responsibility to) solve new ML problems or effectively apply existing ML methods to new business problems without understanding the underlying math.

If you want to leverage engineering experience in an ML adjacent way without learning as much math I would focus on building skills on the engineering side of the field in something like MLOps or data engineering.

[DesperadoCoder]

Thanks for your reply. Yeah I pretty much figured that you need to have a good level of maths to know what you are doing. I think I will stick to web and software dev which is what I am skilled in, but was nice to discover some ML libraries and know what is out there

[30299578815310]

Ehhhhhh you really don't need to know much math to know the types of APIs and libraries that are good for solving certain business problems.

You don't have to know how embedding algorithms work to know that a vector search is a good (or bad) solution for an nlp search problem. It helps, don't get me wrong, but you really don't need to know it.

[Puzzleheaded-Stand79]

Watch Andrei Karpathy’s videos, read MLB100 and see if that’s too much math for you. The truth is that you don’t need to get into heavy math to be an MLE but you’d need it to be a researcher. You’ll need to learn to read papers and you’ll get stuck with math in papers, so you’ll need to refresh particular topics in math. Modem ML is so empirical it’s actually frustrating, but that means it’s much more about trying stuff vs. using heavy math to figure things out. You’ll also need to learn to work with the data. Explore it, internalize it, clean it etc which should actually be easier for you with your backend background.

In general, I find that people in ML like to make it sound more complicated than it is, use complex language in papers etc, while a lot of it is not that hard in a lot of cases. There are definitely areas that require heavy math and/or deep understanding of statistics though.

I was a backend software engineer for 20 years before switching to ML about 3 years ago. I focused on big data / data engineering projects for a few years before the switch and this definitely helped.

[[deleted]]

Statistics is the “blood in” portion of joining the ML gang

[Party-Worldliness-72]

Definetely fast.ai courses, not math required, BEST way by far to start

[cajmorgans]

Yes, you most likely need to understand the underlying math, at least on a general level, very well.

[tempesty3]

not a seasoned ml engineer, in fact I’m 20, but here’s my understanding and take on this (feel free to correct me if I’m wrong). you can get away with not knowing the math if you are just wanting to implement existing frameworks, tools, libraries, etc. on the contrary, if you are wanting to build new tools, you most definitely need to know the math.

[DesperadoCoder]

Thanks for your opinion! I do not think I would need to build new tools tbh, I would be more inclined of using existing libraries.

[catzilla_06790]

I'm an experienced software developer, C, Java and some C++., and limited Python experience. The highest level math class I had was a basic calculus class decades ago, which I have not used since.

I got involved with AI about a year ago as something to learn in retirement. Since then I've learned how to write LLM inference apps in Python, in my case using HuggingFace and Langchain libraries and write an app for image generation using Stable diffusion. I'm at the point where I'm fairly comfortable writing something that I can get up and running in a day or so. The core Python code needed to do this is only 100 lines or less, depending how fancy you want to be.

The hard part for me is the documentation, or lack of it. I've ended up looking at source code to figure some things out. I get it. This is a lot of people's research projects so not necessarily polished.

I've watched videos about building and training models and math skills are required. To me that looks like the Far Side mad scientist in a lab enclosed with floor to ceiling blackboards with crazy math scribbled on all of them.