Machine learning is a set of tools that, broadly speaking, allow us to “teach” computers how to perform tasks by providing examples of how they should be done. For example, suppose we wish to write a program to distinguish between valid email messages and unwanted spam. We could try to write a set of simple rules, for example, flagging messages that contain certain features (such as the word “viagra” or obviously-fake headers). However, writing rules to accurately distinguish which text is valid can actually be quite difficult to do well, resulting either in many missed spam messages, or, worse, many lost emails. Worse, the spammers will actively adjust the way they send spam in order to trick these strategies (e.g., writing “vi@gr@”). Writing effective rules — and keeping them up-to-date — quickly becomes an insurmountable task. Fortunately, machine learning has provided a solution. Modern spam filters are “learned” from examples: we provide the learning algorithm with example emails which we have manually labeled as “ham” (valid email) or “spam” (unwanted email), and the algorithms learn to distinguish between them automatically.

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Using machine learning in practice requires that you make use of your own prior knowledge and experimentation to solve problems. But with the tools of machine learning, you can do amazing things!