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Supervised Machine Learning: Regression

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IntermediateCourse

Learn to build and compare regression models for supervised machine learning. Gain hands-on experience with Python and explore best practices for data analysis. Prerequisites: Python, Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and

4.7 (721 Reviews)

Language

  • English

Topic

  • Machine Learning

Enrollment Count

  • 63.33K

Skills You Will Learn

  • Human Resources, Regression, Machine Learning, Statistical Machine Learning, Supervised Learning

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 5 weeks

Platform

  • Coursera

Last Update

  • April 15, 2025
About this Course
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 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

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.

Instructors

Svitlana Kramar

Data Scientist

I’m a passionate data science educator whose goal is to learn by teaching innovative data science tools that can improve our day-to-day tasks and our quality of life. My interests are in Natural Language Processing: text classification, summarization, and generation. Research can take a long time because there are a lot of resources and new opinions posted every day. Having tools to summarize and extract the information can save a lot of time. I hope we can all learn, approve, and apply the data science tools to cut down on the repetitive and tedious tasks, to make more informed decisions in life, to differentiate fake from real, and to open communication spaces to language-diverse or hearing-impaired audiences. The applications are limitless! My personality: I am a foodie and I love cooking and learning different cuisines. I also love travelling and connecting with people by learning a little bit of their language, about their food and music. I hold Data Science and Analytics master’s degree, specializing in Machine Learning, from University of Calgary.

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