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.
Responsible AI for Developers: Interpretability & Transparency
Responsible AI for Developers: Interpretability & Transparency
This course is part of Responsible AI for Developers Specialization
Instructor: Google Cloud Training
Sponsored by Coursera Learning Team
Gain insight into a topic and learn the fundamentals.
Intermediate level
Some related experience required
3 hours to complete
3 weeks at 1 hour a week
Flexible schedule
Learn at your own pace
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
Shareable certificate
Add to your LinkedIn profile
Assessments
1 assignment
Taught in English
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This course is part of the Responsible AI for Developers Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
Earn a career certificate
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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
Offered by
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