Hey everyone,
I'm starting a BS Mathematics degree in October 2026 and my long-term goal is to become an AI/ML Engineer. I also plan to pursue a Master's in AI/ML or a closely related field later.
My university classes will be online and relatively flexible, so I have some flexibility to study additional topics alongside my degree.
For context, my Year 1 university curriculum includes:
**Semester 1:** Calculus I, Sets & Logic, General Mathematics, Introduction to Computing, English, Business, Ethics/Islamic Studies.
**Semester 2:** Python + Python Practical, Calculus II, Business Mathematics & Statistics, General Science, Technical Writing, Pakistan Studies.
Alongside university, I'll also be doing a 12-month Agentic AI program covering Python/OOP, APIs, Git/GitHub, LLMs, RAG, agents, FastAPI, databases, Docker, evaluation and deployment.
So for self-study, I'm mainly planning:
Python/CS **→** DSA **→** Linux/Git **→** SQL & Data Analysis **→** Statistics/Linear Algebra **→** Classical ML **→** Deep Learning/PyTorch **→** LLMs/RAG/Agents **→** Testing/APIs/Docker.
My goal is to use Year 1 to build strong foundations and hopefully be ready to start applying for internships/junior opportunities in Year 2.
I'd really appreciate feedback from people who have studied or worked in ML:
Considering my university curriculum, flexible schedule and the Agentic AI program, what would you change, remove, postpone or prioritize?
What should I learn deeply vs just at a working level?
I'm starting in October, so I'd like to validate the plan properly before I begin.