I prefer to use the approach "learn as you go". There's nothing to block someone from learning some basics, a library like Scikit Learn and then learn by doing

I prefer to use the approach "learn as you go". There's nothing to block someone from learning some basics, a library like Scikit Learn and then learn by doing examples. But if anyone wants to become an expert into ML/DS, learning statistics and probability is fundamental. Books like A First Course in Probability, Introduction to Statistical Learning and Elements of Statistical Learning, to name a few, are very important. A lot of the mistakes done in practice are based on a lack of understanding in sampling techniques, how statistical metrics can be misleading and so on. First thing I learned in statistics is the difference between quantitative and qualitative information. If someone knows this before hopping into Kaggle, they know that categorical features can't be used as continuous features.