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Introduction to Machine Learning: Regression Models

This workshop focuses on regression models to provide participants with a foundational understanding of machine learning concepts, techniques, and tools used for linear and nonlinear regression. Through a combination of lectures and hands-on exercises, participants will gain practical experience with regression algorithms, one of the most popular machine-learning techniques. The workshop begins with an overview of regression, exploring its various types and applications and continues with training regression models, interpreting the results, and making predictions using real-world datasets. Additionally, participants will gain insights into advanced topics such as regularization and feature selection. By the end of this workshop, you will have a solid understanding of regression models and be familiar with popular Python libraries and tools to implement them.

In this workshop, we will use cloud-based platforms, so you don’t need to have Python installed. Please make sure that you have a Google Colaboratory (https://colab.research.google.com/) account. This workshop will involve hands-on exercises that require the use of programming tools and libraries commonly used in machine learning, such as Python and Scikit-learn. As such, prior familiarity with Python programming is recommended for participants to fully benefit from the practical component of the workshop.

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Location Details

Location:
*ONLINE*

If you have any questions, concerns, or accessibility needs please email eka.grguric@ubc.ca.

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This event is online. Registrants receive the link 24 hours before the event. Registration closes at the same time.
Date:
Wednesday, July 31, 2024
Time:
1:00pm - 3:00pm
Audience:
  All     Faculty     Graduate  
Categories:
  Digital Scholarship     Research Commons  
Presenter(s):
Aisha Eldeeb

Registration is required. There are 95 seats available.

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Presenter(s)

Aisha Eldeeb

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