Name: Interpretable Machine Learning Applications: Part 1
Brand: Coursera Project Network
Availability: OnlineOnly
Rating: 4.4 (8 reviews)

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学生对 Coursera Project Network 提供的 Interpretable Machine Learning Applications: Part 1 的评价和反馈

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课程概述

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....

1 - Interpretable Machine Learning Applications: Part 1 的 8 个评论（共 8 个）

创建者 Reshmi M P

•

Jul 8, 2025

As a newbie to Python and machine learning, I initially found the course quite challenging. However, after going through the material a few times, the concepts began to click. The course does a great job introducing key machine learning techniques like Decision Trees and Random Forests, and it was particularly helpful in explaining how to interpret feature importance and model behavior. While some prior familiarity with Python would definitely help, the hands-on approach using real-world data made the learning process more engaging. This course served as a solid stepping stone for me into the world of interpretable machine learning. I now feel more confident exploring model evaluation and understanding what drives predictions.