The best tutorials for indoctrinating software engineers in ML are written accessibly & use real-world business-case data, or easily comprehended data sets. One

The best tutorials for indoctrinating software engineers in ML are written accessibly & use real-world business-case data, or easily comprehended data sets. One great example of this, from graph theory, was this fantastic 2013 article by Kieran Healy: https://kieranhealy.org/blog/archives/2013/06/09/using-metadata-to-find-paul-revere/ https://kieranhealy.org/blog/archives/2013/06/09/using-metad... There's no getting around the complexities of fit, bias and customized models for many ML problems, so my observation above is obviously limited in its applicability.