IBM - Supervised Machine Learning: Regression
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Supervised Machine Learning: Regression at Coursera Overview
Duration | 11 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Supervised Machine Learning: Regression at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level
- Approx. 11 hours to complete
- English Subtitles: French, Portuguese (European), Russian, English, Spanish
Supervised Machine Learning: Regression at Coursera Course details
- 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.
- This course is part of multiple programs
- This course can be applied to multiple Specializations or Professional Certificates programs. Completing this course will count towards your learning in any of the following programs:
- IBM Introduction to Machine Learning Specialization
- IBM Machine Learning Professional Certificate
Supervised Machine Learning: Regression at Coursera Curriculum
Introduction to Supervised Machine Learning and Linear Regression
Welcome/Introduction Video
Introduction to Supervised Machine Learning: What is Machine Learning?
Introduction to Supervised Machine Learning: Types of Machine Learning
Supervised Machine Learning for Interpretation and Prediction
Regression and Classification Examples
Introduction to Linear Regression
Linear Regression Demo - Part1
Linear Regression Demo - Part2
Linear Regression Demo - Part3
Course Prerequisites
Linear Regression Demo (Activity)
Summary/Review
Check for Understanding
Check for Understanding
End of Module Quiz
Data Splits and Cross Validation
Training and Test Splits
Training and Test Splits Lab - Part 1
Training and Test Splits Lab - Part 2
Training and Test Splits Lab - Part 3
Training and Test Splits Lab - Part 4
Cross Validation
Cross Validation Demo - Part 1
Cross Validation Demo - Part 2
Cross Validation Demo - Part 3
Cross Validation Demo - Part 4
Cross Validation Demo - Part 5
Polynomial Regression
Training and Test Splits Demo
Cross Validation Demo
Summary/Review
Check for Understanding
Check for Understanding
Check for Understanding
End of Module Quiz
Regression with Regularization Techniques: Ridge, LASSO, and Elastic Net
Bias Variance Trade off
Regularization and Model Selection
Ridge Regression
LASSO Regression
Polynomial Features and Regularization Demo - Part 1
Polynomial Features and Regularization Demo - Part 2
Polynomial Features and Regularization Demo - Part 3
Further details of regularization
Details of Regularization - Part 1
Details of Regularization - Part 2
Details of Regularization - Part 3
Polynomial Features and Regularization Demo
Details of Regularization Demo
Summary/Review
Check for Understanding
End of Module Quiz
Supervised Machine Learning: Regression at Coursera Admission Process
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