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Applied Predictive Modeling

Applied Predictive Modeling


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Explore predictive modeling and commonly used models like regressions, clustering, and decision trees that are applied in Python with the scikit-learn package.



Expected Duration (hours)
1.1

Lesson Objectives

Applied Predictive Modeling

  • recognize characteristics of predictive modeling
  • use Python and related data analysis libraries to perform exploratory data analysis
  • recognize key features of Linear and Logistic regressions
  • apply a linear regression with Python
  • apply a logistic regression with Python
  • recognize key features of hierarchical clustering and K-Means clustering
  • apply hierarchical clustering with Python
  • apply K-Means clustering with Python
  • recognize key features of Decision Trees and Random Forests
  • apply a Decision Tree with Python
  • apply a Random Forest with Python
  • apply linear regression, logistic regression, hierarchical clustering, Decision Trees, and Random Forests with Python
  • Course Number:
    it_mlapmldj_01_enus

    Expertise Level
    Intermediate