This course introduces concepts of AI interpretability and transparency. It discusses the importance of AI transparency for developers and engineers. It explores practical methods and tools to help achieve interpretability and transparency in both data and AI models.
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Responsible AI for Developers: Interpretability & Transparency
This course is part of Responsible AI for Developers Specialization
Instructor: Google Cloud Training
Included with
What you'll learn
Define interpretability and transparency as it relates to AI
Describe the importance of interpretability and transparency in AI
Explore the tools and techniques used to achieve interpretability and transparency in AI
Details to know
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There are 4 modules in this course
This module introduces the course structure and objectives.
What's included
1 video
This module focuses on AI interpretability and transparency. It provides various techniques and tools to help achieve interpretability and transparency in both data and AI models.
What's included
9 videos1 assignment1 app item
This module provides a summary of the entire course by covering the most important concepts, tools, and technologies.
What's included
1 video1 reading
Student PDF links to all modules
What's included
3 readings
Instructor
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Frequently asked questions
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.