Course Description
At the core of predictive analytics lie the models used to make predictions after the data has been collected and preprocessed. Explore predictive techniques, including A/B testing, Bayesian Networks, and the support vector machine (SVM).
Learning Objectives
A/B Testing
- start the course
- recognize what A/B testing is and where it is applicable
- establish an A/B test hypothesis and determine what to test
- implement A/B testing for web site optimization
Bayes and Bayesian Belief Networks
- recognize key features of Bayes
- calculate the probability of an event occurring with Bayes
- identify various limitations of Bayes
- recognize features of Bayesian Belief Networks
Support Vector Machines
- identify features of Support Vector Machines
- recognize how to transform linear non-separable data to linear separable data
- determine the optimal hyperplane
Practice: Applying Predictive Approaches
- predict outcomes using A/B testing