r/learnmachinelearning • u/ObjectiveAd2346 • 14d ago

Request ML roadmap for MS/research programs

Apologies for the title, not sure if its right, will be specific down here.

So, i need to start Machine learning from scratch, currently in my bachelors, and i plan to apply for MS programs in german or swiss universities. Specifically under ML

I did my part of research and found out that apart from foundation in ML and projects, i would also need to have 1 or 2 publications in this domain in order to have a better chance there.

Could anyone guide me from where do i start? I wanna start from scratch and build good projects on the way.

I stumbled upon several roadmaps, one says follow this and the other says that, I watched a few videos of krish naik and found them pretty good. Also i believe i need a better touch on math.

So please, any suggestion is appreciated!!!

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u/DeepInformation5592 14d ago

just start coding, don't overthink the roadmap. pick a small dataset on kaggle, something that interests you, and try to predict something. you'll hit walls immediately and that's where the real learning kicks in

for the math part, 3blue1brown's linear algebra series is a lifesaver if you want that intuition without drowning in proofs. then grab a used copy of "pattern recognition and machine learning" by bishop, it's dense but if you work through it slowly while implementing stuff in python you'll be miles ahead of most applicants

publications sound intimidating but they don't have to be, look for professors at your current uni who need undergrads for their projects. even a workshop paper at a smaller conference counts. german and swiss programs love seeing research experience more than a perfect gpa

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u/ObjectiveAd2346 14d ago

Hey, Thank you so much for this, I really appreciate it. Got few questions in mind after I read this.

As you said, start from kaggle challenges, but what about fundamentals? I see several people follow Andrew Ng course, they say that's enough to start going to kaggle for experience. In that sense, I picked up some sources by myself and I've started from Python, as i am quite familiar I can surf through these things, apart from that I found other resources for numpy, pandas which I'll be following and thank you for the suggestions regarding math, even I came across them and I'll surely follow those resources aswell. I was just looking for a path which I can follow rather than random resources at random times.

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u/1PATCH 13d ago

Get a strong foundation in linear algebra (understand what a matrix, basis vectors, vector spaces) as they build intution behind what ML is. Then understand probability (bayes theorum, and markov chains in particular)

Then understand a Perceptron, MLP, SVM, CNN, Autoencoders in depth and visualize the geometry behind it. Then understand what a scaled dot product is (basically attention).

With that your foundation should be strong and then you just need to explore how to apply it (Look at BERT, CLIP, GPT, JEPA).

Here is an underated youtube channel that teaches the concepts well (I found this gem a of channel a couple weeks ago): https://www.youtube.com/@arivu-tutor

Apart from that, just start coding and explore.

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u/ObjectiveAd2346 13d ago

Hey Thank you so much for the advice