In machine learning, we train a computer to find correlations in our data — correlations that we might not find ourselves, or that would be too complex and time-consuming for a human to do. When you engage in a machine-learning project, you need to ensure that your data is reliable, and then choose an appropriate model. Then you need to check your model. It turns out that if you use Python, doing this sort of analysis is fairly straightforward. In this talk, Tenn Leeuwenburg provides a tutorial in machine learning using Python, talking about models, data, neural networks, and the appropriateness (or not) of certain kinds of models to particular situations.
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