I remember clearly my disappointment when I learned, as a college student, how computers were able to play skilled chess. I was taking my first course in artificial intelligence, and had assumed that such a mysterious and powerful topic must have similarly impressive methodologies. In fact, the Minimax algorithm used by Deep Blue and other game-playing computers is quite intuitive (1). Ultimately, though, my disappointment was replaced by excitement: I could build an algorithm to play chess!

I’d like to provide you with that same sense of disappointment regarding machine learning. Machine learning is powerful but needn’t be mysterious. Its basic capabilities, like regression, classification and clustering, are easy to understand, and nontechnical knowledge of those capabilities is sufficient to make clear requests of data scientists.

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