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.
Upon completing this course, you will be able to:
Describe, compare, and contrast the three main approaches to probability
Understand the fundamentals of the Bayesian approach—such as conditional probability, priors, and updating beliefs
Apply Bayesian methods such as Bayes theorem and contingency tables to simple problems
Describe two Bayesian machine learning methods—multinomial and gaussian Bayes classifiers
Recognize the benefits of using these machine learning methods for modeling complex scenarios
Evaluate the results of the machine learning tests against business goals in Python
Why stop here? Expand your skills and show your expertise with the professional certifications, specializations, and CPE credits you’re already on your way to earning.