No one has yet won the "These are the best resources for learning ML" aggregator and a lot of people are interested in and building them. So people keep posting them.

Many people (especially students from universities) keep asking me and my coworkers at YerevaNN about good educational resources. They have different levels of math background, some want to study theory and watch visualizations, others want to play with the code before reading formulas..

At some point we understood it's better to spend some time and build a guide that will cover most of these questions (and, as always, we spent a lot more time on this than we expected)

I think OP is asking, why spend time writing another guide when there's plenty that suffice, including full books and papers on the topic?

I was looking for something that would answer common questions for some time, but couldn't find anything. Pointing students to research papers unfortunately doesn't work in most of the cases. Deep learning book is too hard for some and they want to see a comparison of different sources.