Hi everyone,
I know AI is becoming a huge part of almost every field, and academia seems to be integrating AI into everything. I’m currently pursuing a Master’s degree in Cybersecurity, and my thesis will likely involve AI, Machine Learning, and/or Deep Learning for cybersecurity.
So far, I’ve completed "AI for Everyone" by Andrew Ng, and I’ve just started the "Machine Learning Specialization". However, whenever the course gets into the mathematical details, I find myself getting lost and feeling overwhelmed.
My main goal isn’t to become an AI researcher. I want to learn the practical side first—how to find and prepare datasets, clean and preprocess them, choose the right model or algorithm for a problem, train it, evaluate it, and apply it to a real cybersecurity use case. I understand that mathematics is important and that it provides a deeper understanding, but right now it feels like it’s slowing down my progress.
For those of you who have been in a similar situation, especially if you came from a cybersecurity or non-AI background:
- Did you focus on the practical side first and come back to the math later?
- How much math do you think is truly necessary before building real ML/DL projects?
- What learning path would you recommend for someone who wants to apply AI to cybersecurity research?
I’d really appreciate hearing about your experiences and any advice you have. Thank you!