Python for Machine Learning (ML) 1: Univariate Linear Regression (ONLINE)
Python for Machine Learning (ML) 1: Univariate Linear Regression (ONLINE) Online
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.
Fundamentals of supervised learning in Python; applying a rudimentary ML model using univariate linear regression (i.e., one feature):
-- Overview: “What is Machine Learning?”
-- Univariate Linear Regression Model
-- Mean-Squared Error Cost Function
-- Gradient Descent Algorithm for Linear Regression
Prerequisites: Python & Data Workshop Series 0-3: https://lib.gsu.edu/rds-recordings
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.
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- Friday, March 24, 2023
- 1:00pm - 3:00pm
- Time Zone:
- Eastern Time - US & Canada (change)
- Downtown Campus Library
- This is an online event. Event URL will be sent via registration email.