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 3 of a 3 part series. Topics covered in part 3:
Fundamentals of supervised learning in Python; applying an ML model using logistic regression (e.g., classification prediction).
-- Logistic Regression Model
-- Cost Function for Logistic Regression
-- Gradient Descent for Logistic Regression
-- Hyperparameter Intuition
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
