Darren Cook explores the main types of machine-learning algorithms, describing the kinds of task each is suited to, the explainability, repeatability, scalability, training time, sensitivity to data issues, and downsides of each, and the types of answers you can hope to get from them. Whether you are detecting fraud, detecting cancer, building driverless cars, or making AlphaGo, something in this talk will apply to you.
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