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Predictive Analytics I – Machine Learning Tools
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From Statistics.Com:

“In this online course, “Predictive Analytics 1 – Machine Learning Tools,” you will be introduced to the basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining. This course covers the two core paradigms that account for most business applications of predictive modeling: classification and prediction. In both cases, predictive modeling takes data where a variable of interest (a target variable) is known and develops a model that relates this variable to a series of predictor variables, also called features. In classification, the target variable is categorical (“purchased something” vs. “has not purchased anything”). In prediction, the target variable is continuous (“dollars spent”). You will learn how to explore and vizualize the data, to get a preliminary idea of what variables are important, and how they relate to one another. Four modeling techniques will be used: k-nearest neighbors, classification and regression trees (CART), and Bayesian classifiers. Then you will learn how to combine different models to obtain results that are better than any of the individual models produce on their own. The course will also cover the use of partitioning to divide the data into training data (data used to build a model), validation data (data used to assess the performance of different models, or, in some cases, to fine tune the model) and test data (data used to predict the performance of the final model). The course includes hands-on work with XLMiner, a data-mining add-in for Excel.”

Recommended Prerequisites: Statistics, Probability and Study Design, Inference and Association

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