Guidance Needed on My ML Learning Path
Main question: am I progressing in a reasonable direction, or am I approaching ML too chaotically?
First, a small warning:
This is my very first time uploading something here... And I’m not a native English speaker, and my writing skills are rough, so I apologize in advance if this post feels messy.
I’m not from a CS/ML major, and I’m definitely not a professional. Most of what I’ve learned so far has been through self-study. Still, I’ve been trying to build proper foundations instead of only consuming surface-level tutorials.
My original motivation for learning ML came from biology-related applications — things like protein structure prediction, AlphaFold, molecular simulation, etc.
But while learning, another interest gradually started growing:
understanding how the human brain works, and whether parts of those mechanisms can somehow be mimicked through ANN architectures.
Because of those broad goals, I sometimes feel like I’m progressing while also wandering around blindly at the same time.
So far, I’ve mainly focused on building mathematical foundations first.
Math background:
• Linear Algebra
- vectors and linear transformations
- independence / orthogonality
- eigenvectors & eigendecomposition
- PCA and related concepts
• Probability & Statistics
(mainly through edX Probability: The Science of Uncertainty and Data)
- probability distributions
- Bayes rule
- random variables
- statistical reasoning
• Calculus
Thankfully I had decent exposure to it in high school, and later reinforced it through additional self-study and various online lectures.
After revising these subjects several times, I started following Stanford CS229.
Honestly, the first time I touched it, I panicked and went back to relearn the basics again. But after returning later, the lectures became much more understandable.
At least now, when I read about things like Transformers or Attention mechanisms, the terminology no longer feels completely alien.
Alongside theory, I’m also learning PyTorch.
I already had some Python background before this, which helped a lot.
I’ve also been following some DeepLearning.AI material.
Another unusual thing:
before learning ML properly, I actually jumped into a short internship involving protein-prediction ML work. Most of my later math/ML study happened after that experience, because it made me realize very clearly what I did not understand.
I’ve also worked a bit with quantum circuit modeling during a domestic competition connected to that internship. Different field, yes, but surprisingly some of the mathematical thinking still helps.
So overall:
- am I approaching this reasonably?
- is my current balance between math / theory / implementation okay?
- what would you recommend focusing on next?
Any advice is welcome — especially from people who entered ML from non-traditional backgrounds.