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Machine learning is the art of teaching computers to learn and act on specific things without having to be programmed. Andrew Ng at Stanford says "In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome."
Ever wanted to go beyond your linear model in Microsoft Excel? This class is a high-level introduction to machine learning models and two types of algorithms: supervised (regression) and unsupervised (classification) models.
By the time you finish this class, you'll have an idea of different types of algorithms and models, and situations in which you might want to use them. You’ll also have some resources and advice on how to specialize in machine learning, or become literate enough to hire a machine learning specialist in your organization.
Beginner/Intermediate. This is a general survey of machine learning algorithms and is not meant as a theoretical introduction, or technical construction of these algorithms. This class will be more applied and comprehensive than it is rigorous and theoretical. Resources on where you can learn more of the theory will be provided.