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This course delves into Logistic Regression and its industry applications, covering both univariate and multivariate models in detail
17 hours of learning
Linear Regression
ROC
Data Manipulation
What You Will Learn
This session covers logistic regression basics through a univariate example, including binary classification, the sigmoid function, likelihood, odds, log odds, and model building in Python.
Topics Covered
Explore multivariate logistic regression for better predictions by using multiple variables, akin to linear regression, focusing on capturing complexities in the target variable.
Topics Covered
This session covers key metrics beyond accuracy for evaluating logistic regression models, identifying optimal scenarios for performance, and making predictions on test datasets to gauge model effectiveness.
Topics Covered
This session applies logistic regression concepts to business settings, building on earlier lessons about model creation and evaluation in Python. We'll explore various types and nuances of logistic regression.
Topics Covered
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Receive a signed and verifiable e-certificate from upGrad upon successfully completing the course.
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