INTRODUCTION TO APPLIED MACHINE LEARNING

Catalog description

Explores and applies machine learning models and methods including unsupervised learning and supervised learning. Focuses on gathering, cleaning, and preparing data for various analyses. Distinguishes between unsupervised methods including clustering, and dimensionality reduction and supervised modeling methods including Decision Trees, Random Forest, Naive Bayes, Support Vector Machines, and Regression). Covers Training and Testing, Confusion Matrices, x-fold cross validation, visualization options, decision science, ethical considerations, and data communication. Uses Python, Sklearn, and related Python packages/libraries.