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Supervised ML: Regression

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Besides Python programming and data science fundamentals, the course covers supervised machine learning regression, which includes training models for continuous outcomes, error metrics, Elastic Net, LASSO, Ridge regularization, and data science fundamentals for aspiring data scientists.

Key AI Functions:

Linear Regression,Machine Learning (ML) Algorithms,Ridge Regression,Supervised Learning,Regression Analysis

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Description for Supervised ML: Regression

Learn about regression in supervised machine learning, covering training models for continuous outcomes, error measures, and Elastic Net, LASSO, and Ridge regularization.

Learn to discern classification and regression applications, develop and apply linear regression models, and choose error metrics.

Regularization prevents overfitting; use it to improve regression models.

Aspiring data scientists who want to use supervised machine learning regression in business must know data cleaning, exploratory data analysis, mathematics, linear algebra, probability, statistics, and Python programming.

Level: Intermediate

Certification Degree: Yes

Languages the Course is Available: 22

Offered by: On Coursera provided by IBM

Duration: 20 hours (approximately)

Schedule: Flexible

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