Course details

Machine Learning, Propensity Score, & Segmentation Modeling

Machine Learning, Propensity Score, & Segmentation Modeling


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



Overview/Description
Both supervised and unsupervised machine learning techniques are at the forefront of the predictive analytics and data mining industry. Discover machine learning features and tools, and explore propensity scoring and segmentation modeling.

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)
0.9

Lesson Objectives

Machine Learning, Propensity Score, & Segmentation Modeling

  • start the course
  • identify key features of machine learning
  • identify key tools used for machine learning and the high-level process steps
  • identify key features of deep learning
  • distinguish between supervised and nonsupervised learning methods
  • identify key features of ensemble techniques
  • measure ensemble error rate
  • recognize key features of the propensity score
  • identify key features of propensity score matching
  • estimate treatment effects
  • apply propensity score matching
  • identify key features of segmentation modeling
  • distinguish between exploratory data analysis and cluster segmentations
  • Course Number:
    df_prma_a15_it_enus

    Expertise Level
    Intermediate