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Understanding and Applying Logistic Regression

Course Summary

This course will teach you both the theory and implementation of logistic regression, in Excel (using solver), Python, and R.


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    Course Syllabus

    Course Overview
    - 1m 48s

    —Course Overview 1m 48s
    Modeling Relationships Between Variables Using Regression
    - 36m 38s

    —Playing the Odds with Logistic Regression 6m 37s
    —Working Smart with Logistic Regression 4m 25s
    —Applications of Logistic Regression - Analysis, Allocation 5m 21s
    —Applications of Logistic Regression - Prediction, Classification 4m 8s
    —Logistic Regression and Linear Regression - Similarities 4m 21s
    —Logistic Regression and Linear Regression - Differences 4m 44s
    —Logistic Regression and Machine Learning 6m 59s
    Understanding Logistic Regression Models
    - 33m 39s
    Implementing Logistic Regression Models in Excel
    - 29m 41s
    Implementing Logistic Regression Models in R
    - 23m 0s
    Implementing Logistic Regression Models in Python
    - 17m 57s


Course Fee:
USD 29

Course Type:

Self-Study

Course Status:

Active

Workload:

1 - 4 hours / week

This course is listed under Open Source , Development & Implementations and Data & Information Management Community

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