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This course introduces k-means clustering and logistic regression, which are used to uncover natural groupings in data and to model the probability of membership in a set of classes, respectively.
It explores two applications of these methods, using k-means clustering to segment observations into meaningful clusters and logistic regression to classify instances and predict the likelihood of specific categories. Lessons cover choosing the optimal number of clusters, comparing the inertia of different clusterings, and minimizing likelihood with stochastic gradient descent. By the end, you'll understand how and when to deploy these methods and how to derive insights from their results.
It explores two applications of these methods, using k-means clustering to segment observations into meaningful clusters and logistic regression to classify instances and predict the likelihood of specific categories. Lessons cover choosing the optimal number of clusters, comparing the inertia of different clusterings, and minimizing likelihood with stochastic gradient descent. By the end, you'll understand how and when to deploy these methods and how to derive insights from their results.
