I don't know, just check out a few Kaggle competitions and how pragmatic the winning teams are approaching their solution. It's most often a combination of tried-and-true techniques, used in an ensemble, with some smart feature selection. Anecdotally, there's plenty of ready-to-use ML tech available nowadays that I, as a novice, was able to go from zero to working Gradient Boosting classifier within a few days. For me that's the definition of applying the techniques without trying to earn a PhD in the field.