Course details

Predictive Modeling: Implementing Predictive Models Using Visualizations

Predictive Modeling: Implementing Predictive Models Using Visualizations


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Explore how to work with machine learning feature selection, general classes of feature selection algorithms, and predictive modeling best practices. In this 12-video course, learners discover how to implement predictive models with scatter plots, boxplots, and crosstabs by using Python. Key concepts examined here include the benefits of feature selection and the general classes of feature selection algorithms; the different types of predictive models that can be implemented and associated features; and how to implement scatterplots and the capability of scatterplots in facilitating predictions. Next, you will learn about Pearson's correlation measures and the possible ranges for Pearson's correlation; learn to recognize the anatomy of a boxplot, a visual representation of the statistical five-number summary of a given data set; and observe how to create and interpret boxplots with Python. Then see how to implement crosstabs to visualize categorical variables; learn statistical concepts that are used for predictive modeling; and learn tree-based methods used to implement regression and classification. Finally, you will learn best practices for implementing predictive modeling.



Expected Duration (hours)
0.7

Lesson Objectives

Predictive Modeling: Implementing Predictive Models Using Visualizations

  • Course Overview
  • list the benefits of feature selection and the general classes of feature selection algorithms
  • recall the different types of predictive models that can be implemented and features
  • implement scatter plots and describe the capability of scatter plots in facilitating predictions
  • define Pearson's correlation measures and specify the possible ranges for Pearson's correlation
  • recognize the anatomy of a boxplot
  • create and interpret boxplots using Python
  • implement crosstabs to visualize categorical variables
  • describe statistical concepts that are used for predictive modeling
  • demonstrate the tree-based methods that can be used to implement regression and classification
  • describe the best practices for implementing predictive modeling
  • implement boxplots, scatter plots, and crosstabs using Python
  • Course Number:
    it_mlfupddj_02_enus

    Expertise Level
    Intermediate