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One of the most common tasks performed by data scientists and analysts is prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide a basic foundation in concepts such as training and testing sets, overfitting, and error rates. The course will also introduce a range of algorithmic and model-based machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the entire process of creating prediction features, including data collection, feature creation, algorithms, and evaluation.
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Associate Professor, Biostatistics
Bloomberg School of Public Health
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