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:
- 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.