As an aspiring AI engineer, I’ve been wondering how to design my learning path to become proficient in building and deploying intelligent systems. With a solid background in programming and basic knowledge of linear algebra, calculus, and probability, I’m torn between investing time in data science fundamentals or diving straight into AI/ML-focused libraries and frameworks.
My goal is to work professionally in applied AI, building actual models, integrating them into systems, and potentially contributing to open-source or freelance projects in the future. But I’m unsure about the best approach to get there.
Do I need to master data science (Pandas, Seaborn, basic statistics, etc.) to be a successful AI engineer? Or can I start hands-on with AI libraries and fill in data science knowledge as needed? How do AI engineers usually balance their time between theory, tooling, and project-based learning?
I’d love to hear from people working in AI/ML engineering roles about their experiences and recommended resources. Are there any well-designed learning roadmaps or university course structures that emphasize this specific engineering-oriented AI track?
Any insights or advice would be greatly appreciated.