Python for Machine Learning (ML) 2: Multiple Linear Regression

From Amanda Swygart-Hobaugh April 11th, 2023

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This applied Machine Learning (ML) series introduces participants to the fundamentals of supervised learning and provides experience in applying several ML algorithms in Python. Participants will gain experience in regression modeling; assessing model adequacy, prediction precision, and computational performance; and learn several tools for visualizing each step of the process.

This is part 2 of a 3 part series. Topics covered in part 2:

Fundamentals of supervised learning in Python; applying an ML model using multivariate regression (i.e., multiple features).

-- Multiple Linear Regression Model

-- Vectorization

-- Gradient Descent for Multiple Linear Regression

-- Feature Scaling

Python for Machine Learning workshop materials: Google Colab

Additional Github workshop materials: Github

Research Data Services (RDS) homepage: https://lib.gsu.edu/data

RDS Recorded Workshops : https://lib.gsu.edu/rds-recordings

RDS workshop attendance check-in & feedback form : https://lib.gsu.edu/rds-check-in

GSU Students, Faculty, and Staff -- Earn a GSU Data Ready! Badge digital micro-credential for attending the entire workshop series -- more information here : https://lib.gsu.edu/data-ready

Tags: research data servicespython (programming language)machine learning

Appears In: Research Data Services (RDS)

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