AI engineer learning path

Hi, I am trying to become AI engineer (the specific field is not selected yet). From all the research I have done the most balanced path is the following one:

  1. Math (linear algebra, calculus, probability) → 2. Statistics → 3. Python → 4. Machine Learning → 5. Doing Kaggle projects. Now, I have relatively good foundation in linear algebra, calculus and python, and shaky foundation in probability and statistics. Can I jump right into a ML course and go back to those gaps whenever I face a problem, that points to that gap, during the course? I prefer the approach “first practice and then theory as required” because I want to walk the shortcut to the job instead of indulging myself in this neat structured learning that is efficient without any doubt but is time-consuming for my circumstances.

Yes. You don’t really need a lot of calculus or statistics to get started. If you understand the concepts of “mean” and “standard deviation”, and you know what uniform and normal random variables are, you’re good to get started.

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