Decision Tree Learning

Decision Tree Learning is a method of approximating discrete-valued target functions, in which the learned function is represented by a decision tree. Coursera's Decision Tree Learning catalogue will guide you in understanding this supervised learning method extensively used in machine learning and data mining. You'll learn how to build, visualize, and optimally prune decision trees for prediction and classification. This catalogue will also teach you about attribute selection measures, overfitting, randomness, and ensemble methods within decision tree learning. In mastering this skill, you'll be equipped to solve complex problems in areas such as finance, healthcare, and natural language processing using decision tree learning algorithms.