There is now tons of material available on how to do certain (most popular) ML tasks and what kind of output you can expect.
However I found that resources on how to select appropriate ML task/approach given specific problem are very coarse and scarce. I can't find anything better than "use rnn/lstm for time series prediction" or "k-means for classification"
Are there publications/Internet resources available that dedicated purely to teaching how to
- define you problem in a way that would suit specific ML approach
- select best ML model within the approach?