Course · Intermediate
Logistic Regression Modeling in Python
In this course, you'll learn how to build and evaluate logistic regression models, both from scratch and using scikit-learn. You'll learn how to distinguish between regression and classification and how to interpret and apply model results to address classification problems.
- Intermediate friendly
- 3 hrs
- 4 lessons
- 1 project
- Premium
Course overview
Classify and interpret categorical outcomes by constructing, evaluating, and applying logistic regression models for inference and prediction.
What's inside
4 lessons · 1 project
- 01 66 minIntroduction to Logistic Regression
<p>This lesson explores classification, including the logistic model structure. It will also compare linear regression to emphasize differences in the problems.</p>
- 02 32 minInterpreting the Regression Parameters
<p>In this lesson, learners will build a linear regression model using scikit-learn They’ll also learn to interpret the important attributes of a logistic model, including the regression coefficients.</p>
- 03 25 minEvaluating Logistic Regression Models
<p><span style="color: rgba(0,0,0,0.87);background-color: rgb(255,255,255);font-size: medium;">In this lesson, you'll learn the different methods for evaluating classification models. You'll learn how to calculate and interpret different evaluation metrics for logistic regression.</span></p>
- 04 30 minApplying Logistic Regression Models
<p>How to use a developed model for classification tasks.</p>
- 05 20 minGuided Project: Classifying Heart Disease Project
For this project, you'll assume the role of a medical researcher aiming to develop a logistic regression model to predict heart disease in patients based on their clinical characteristics.
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