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Despite the widespread usage of machine learning throughout organizations,there are some key principles that are commonly missed. In particular: 1) Thereare at least four main families for supervised learning: logical modelingmethods, linear combination methods, case-based reasoning methods, anditerative summarization methods. 2) For many application domains, almost allmachine learning methods perform similarly (with some caveats). Deep learningmethods, which are the leading technique for computer vision problems, do notmaintain an edge over other methods for most problems (and there are reasonswhy). 3) Neural networks are hard to train and weird stuff often happens whenyou try to train them. 4) If you don't use an interpretable model, you can makebad mistakes. 5) Explanations can be misleading and you can't trust them. 6)You can pretty much always find an accurate-yet-interpretable model, even fordeep neural networks. 7) Special properties such as decision making orrobustness must be built in, they don't happen on their own. 8) Causalinference is different than prediction (correlation is not causation). 9) Thereis a method to the madness of deep neural architectures, but not always. 10) Itis a myth that artificial intelligence can do anything.

The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis