Back to Interpretable Machine Learning Applications: Part 1
Coursera

Interpretable Machine Learning Applications: Part 1

In this 1-hour long project-based course, you will learn how to create interpretable machine learning applications on the example of two classification regression models, decision tree and random forestc classifiers. You will also learn how to explain such prediction models by extracting the most important features and their values, which mostly impact these prediction models. In this sense, the project will boost your career as Machine Learning (ML) developer and modeler in that you will be able to get a deeper insight into the behaviour of your ML model. The project will also benefit your career as a decision maker in an executive position, or consultant, interested in deploying trusted and accountable ML applications.

Status: Model Evaluation
Status: Applied Machine Learning
BeginnerGuided Project2 hours

Featured reviews

VM

5.0Reviewed Aug 6, 2022

Pretty Informative and crisp to the point. Great hands on course.

CG

4.0Reviewed Sep 25, 2025

The pdp library did not match the project requirements

All reviews

Showing: 9 of 9

Reshmi Mary Paul
4.0
Reviewed Jul 8, 2025
Chip Griffith
4.0
Reviewed Sep 26, 2025
Pascal Uriel ELINGUI
5.0
Reviewed Jul 1, 2021
Venkataramana Madugula
5.0
Reviewed Aug 7, 2022
Francis Dakubo
5.0
Reviewed Jun 14, 2023
Abhinav Pal
5.0
Reviewed Mar 20, 2026
Samuel Yomi-Faseun
5.0
Reviewed Feb 23, 2025
Srinivas Ghodke
4.0
Reviewed Dec 14, 2024
Ashritha D S
4.0
Reviewed Oct 25, 2024