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Course Description

This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques.By the end of this course you should be able to:Differentiate uses and applications of classification and regression in the context of supervised machine learning Describe and use linear regression modelsUse a variety of error metrics to compare and select a linear regression model that best suits your dataArticulate why regularization may help prevent overfittingUse regularization regressions: Ridge, LASSO, and Elastic net Who should take this course?This course targets aspiring data scientists interested in acquiring hands-on experience  with Supervised Machine Learning Regression techniques in a business setting. What skills should you have?To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.
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Enroll Now - Select a section to enroll in

Section Title
Supervised Machine Learning: Regression
Language of Delivery
English
Section Schedule
Date and Time TBA
Course Fee(s)
Course Fee non-credit $49.00
Drop Request Deadline
TBD
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