The final two phrases "how to use the data to make it as useful as possible to the algorithm, and how to fine-tune the parameters that each of these algorithms

The final two phrases "how to use the data to make it as useful as possible to the algorithm, and how to fine-tune the parameters that each of these algorithms take" are just as influential in the quality of any ML process as the selection of training method. The data type and quantity, overfitting, sparseness, dataset shift, etc. are all constant challenges that affect the classification and require work to deal with. When you see the effort involved at times in data wrangling, feature selection, equation optimization or parameter experimentation to overcome deficiencies in training sets, any notion of 'black magic' fades.