I have a question regarding preparation for a high-paying role in AI/ML. Specifically, I’m trying to understand the appropriate balance between theoretical knowledge and practical skills.
For instance, how deep should one go into the mathematical and theoretical foundations of machine learning? Alternatively, would it be more beneficial to prioritize hands-on experience through projects, real-world implementations, and industry-recognized certifications?
If both theoretical understanding and practical experience are important, what would be the ideal balance between them when preparing for a strong career in this field?
Hi @xgltn Great question! What draws you to this space? Is it the money or is there something else? “High-paying role in AI/ML” covers a big range of very different careers, so,it might help to look at your current strengths. What’s your background?
I think both theory and practice are important, but for industry roles practical experience usually matters more. Building real projects and working with real data helps in understanding how machine learning works in real-world scenarios.