Attend ONLINE: https://gsumeetings.webex.com/meet/dforsberg1

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 series consists of three (3) workshops. For individuals who are new to Python and/or Google Colab, it is highly recommended that you first complete the prerequisite Python & Data Workshop Series 0-3 workshops. For those who are new to Machine Learning, it is highly recommended that the workshops in this series be attended in sequential order. While these workshops are taught exclusively using code (i.e., there are no point-and-click methods), attendees do not need to have any prior experience with programming, coding, or scripting. All are welcome.

Workshop Topics:

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

-- Overfitting & Model Adequacy

Prerequisites: Python & Data Workshop Series 0-3: https://lib.gsu.edu/rds-recordings, Python for Machine Learning (ML) 1: Univariate Linear Regression Workshop, and Python for Machine Learning (ML) 2: Multivariate Linear Regression Workshop.

Software Requirements for Hands-on Participation:

For participants wishing to follow along with the “hands-on” portion of the workshop, please see the directions at the following url: https://research.library.gsu.edu/python/workshop

NOTE: Please read our Workshops ~ Etiquette & Policies page for pertinent information to your workshop attendance.

Presenter(s): Dan Forsberg (MA, MS), Data Services Specialist with the Library's Research Data Services Team

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