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Gear up for jobs in high-demand fields: data analytics, digital marketing, and more.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 learningÂ
Describe and use linear regression models
Use a variety of error metrics to compare and select a linear regression model that best suits your data
Articulate why regularization may help prevent overfitting
Use regularization regressions: Ridge, LASSO, and Elastic net
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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.
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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.