While interviewing candidates I have observed an influx of candidates having a strong bias towards neural networks in the recent years - i.e. not understanding concepts such as overfitting in general and telling me how dropout would fix that.

We are a mid-size AI/ML consultancy company. We have not yet have a project where we'd actually been given enough data and resources from a customer to be even able to learn a NN from scratch. Vast majority of the problems we tackle are easily solvable with traditional ML methods which these fresh candidates consider obsolete and boring.

This tutorial pretends to be mildly aware of that, yet the first course it offers is Andrew Ng's ML, which is structured in a way that it culminates in building a NN, a brief mention of RF and then it jumps straight into DNN.

A disclaimer that this is hardly a thing a person would actually do for living should be included at the beginning of every tutorial such as this one.