A/B Testing, Bayesian Networks, and Support Vector Machine

A/B Testing, Bayesian Networks, and Support Vector Machine

Overview/Description
Target Audience
Prerequisites
Expected Duration
Lesson Objectives
Course Number
Expertise Level

Overview/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).

Target Audience
All individuals who are new to predictive analytics and wish to use it to optimize their business performance; business leaders; analysts; marketing, sales, software, and IT professionals who want to add predictive analytics to their skill set; and decision makers of any kind

Prerequisites
None

Expected Duration (hours)
0.8

Lesson Objectives

A/B Testing, Bayesian Networks, and Support Vector Machine

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

recognize key features of Naïve Bayes

calculate the probability of an event occurring with Naïve Bayes

identify various limitations of Naïve Bayes

recognize features of Bayesian Belief Networks

identify features of Support Vector Machines

recognize how to transform linear non-separable data to linear separable data

determine the optimal hyperplane

predict outcomes using A/B testing

Course Number:
df_prma_a10_it_enus

Expertise Level
Intermediate