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Machine Learning with Python: Logistic Regression

1h 18mIntermediate2022-11-09

Authors

Frederick Nwanganga

Information Technology Professional and Teacher

Course details

Are you looking for a practical way to use machine learning to solve complex real-world problems? Logistic regression is an approach to supervised machine learning that models selected values to predict possible outcomes. In this course, Notre Dame professor Frederick Nwanganga provides you with a step-by-step guide on how to build a logistic regression model using Python. Learn hands-on tips for collecting, exploring, and transforming your data before you even get started. By the end of this course, you’ll have the technical skills to know when and how to design, build, evaluate, and effectively manage a logistic regression model all on your own.

Skills covered

Machine LearningPythonArtificial Intelligence (AI)Programming LanguagesOpen SourceSoftware DevelopmentDeep Dive (X:Y)

Concepts

0. Introduction

01 - Classifying data with logistic regression
02 - What you should know
03 - Using the exercise files
04 - Using GitHub Codespaces with this course

1. Regression

05 - What is regression
06 - The anatomy of a regression model
07 - Common types of regression

2. Logistic Regression

08 - What is logistic regression
09 - Making predictions with logistic regression
10 - Interpreting the coefficients of logistic regression
11 - Why and when to use logistic regression

3. Classifying Data with Logistic Regression

12 - How to explore data for logistic regression in Python
13 - How to prepare data for logistic regression in Python
14 - How to build a logistic regression model in Python
15 - How to interpret a logistic regression model in Python

Conclusion

16 - Next steps