Regression, Classification, and Clustering (Pilot)

SUMMARY

This lesson introduces the common tasks of supervised learning – regression and classification – and unsupervised learning – clustering. Its goal is to provide a general understanding of the types of tasks can be solved and how to match problems with the appropriate solutions. This serves as an introduction to key concepts, preparing you for a deeper dive into important algorithms later in the course.

In this lesson, we’ll explore

1. What problems regression, classification, and clustering solve.
2. What the difference between regression and classification methods.
3. Real-world examples of their applications.

About The Author

Thi Hong Nhung Dang (Nhung) is an engineer in AI at the Grenoble Institute of Technology (Grenoble INP), France. She approaches manufacturing challenges by not only leveraging data but also considering human factors, economic perspectives, and the broader context to deliver well-rounded machine learning solutions.