- Bayesian Thinking Course Overview
Bayesian methods give us an alternative way to think about probability, with applications in business decision-making.
While traditional statistics requires us to observe a meaningful sample to inform decisions, Bayesian methods allow a “best guess” approach based on available information. These approaches also allow us to include other information such as beliefs and outside knowledge.
This course will take you on a step-by-step journey, from traditional statistical approaches, through conditional probability and Bayes Theorem. These concepts will form a foundation to help you understand two basic Machine-Learning examples introduced in the course. In the end, you’ll produce a real-world classification model using Python.
Bayesian Thinking Objectives
Upon completing this course, you will be able to:
Describe, compare, and contrastthe three main approaches to probabilityUnderstand the fundamentals of the Bayesian approach—such as conditional probability, priors, and updating beliefsApply Bayesian methods such as Bayes theorem and contingency tables to simple problemsDescribe two Bayesian machine learning methods—multinomial and gaussian Bayes classifiersRecognize the benefits of using these machine learning methods for modeling complex scenariosEvaluate the results of the machine learning tests against business goals in Python
