Hey folks! I'm a second-year BTech student majoring in Computer Science with a focus on AI and Machine Learning. I'm really drawn to the exciting world of ML and AI, but I have to admit, I'm quite weak in math—particularly when it comes to probability and linear algebra, which can be pretty daunting. Right now, I've been learning Java and Data Structures along with the basics of Python. My main goal is to secure a good job in tech, especially in this AI-driven era, but I'm feeling pretty confused about the best path to take. Should I dive into ML despite my math struggles, or would it be smarter to pivot towards fields like full-stack or backend development? I'm also wondering if it's feasible to get better at math while gradually progressing in ML. What skills should I really be honing right now to stay relevant in the AI space? I just don't want to waste my time flipping between different areas without a clear direction. I'd really appreciate any honest insights from those with experience!
2 Answers
Definitely consider exploring adjacent fields like DevOps or ML Ops! These areas often need solid engineering skills to build and deploy models, and you can get started with more basic backend knowledge. It’s a great way to stay close to ML without getting deep into complex math right away. If you're interested in the creative side of tech, you might find a path that suits you well!
It's important to have a solid grasp of math for machine learning, but don't let that discourage you! Struggling in math is common, and many people improve with practice. If you love ML, keep at it and work on your math skills concurrently. Remember, there are roles in tech that don't require deep math knowledge, like DevOps or ML Ops, where you can use your coding skills effectively. It’s all about finding what you enjoy and focusing on your strengths while you build your weaknesses!
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