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Supervised Machine Learning: Regression

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Course Features

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Duration

11 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

11 hours

Course Description

This course will teach you about Regression, which is one of the most important types supervised machine-learning modelling families. This course will show you how to create regression models that predict continuous outcomes. For comparisons between models, you'll also be able to use error metrics. You will also learn best practices like regularization and train and test splits.

Course Overview

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International Faculty

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Case Based Learning

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Post Course Interactions

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Case Studies,Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

What You Will Learn

Articulate why regularization may help prevent overfitting

Describe and use linear regression models

Differentiate uses and applications of classification and regression in the context of supervised machine learning

Use a variety of error metrics to compare and select a linear regression model that best suits your data

Use regularization regressions: Ridge, LASSO, and Elastic net

Target Students

This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Regression techniques in a business setting.

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