Machine Learning, Propensity Score, & Segmentation Modeling

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

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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