Supervised Learning: Regression (Coursera)

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.

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

Syllabus

WEEK 1

Introduction to Supervised Machine Learning and Linear Regression

This module introduces a brief overview of supervised machine learning and its main applications: classification and regression. After introducing the concept of regression, you will learn its best practices, as well as how to measure error and select the regression model that best suits your data.

WEEK 2

Data Splits and Cross Validation

There are a few best practices to avoid overfitting of your regression models. One of these best practices is splitting your data into training and test sets. Another alternative is to use cross validation. And a third alternative is to introduce polynomial features. This module walks you through the theoretical framework and a few hands-on examples of these best practices.

WEEK 3

Regression with Regularization Techniques: Ridge, LASSO, and Elastic Net

This module walks you through the theory and a few hands-on examples of regularization regressions including ridge, LASSO, and elastic net. You will realize the main pros and cons of these techniques, as well as their differences and similarities.