Machine learning algorithms come with the promise of being able to figure out how to perform important tasks by learning from data, i.e., generalizing from examples without being explicitly told what to do. This means that the higher the amount of data, the more ambitious problems can be tackled by these algorithms. However, developing successful machine learning applications requires quite some “black art” that is hard to find.

Let’s go through some of the lessons learned by machine learning researchers and practitioners (put together in a great research paper by Professor Pedro Domingos), so that we can avoid some of the major pitfalls.

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