Course details

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


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