10
Responsible AI for Developers: Interpretability & Transparency
10
Responsible AI for Developers: Interpretability & Transparency
These skills were generated by AI. Do you agree this course teaches these skills?
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.
Course Info
Objectives
- 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
Prerequisites
Working knowledge of machine learning concepts and practices. Working knowledge of machine learning pipelines and tools. Prior experience with programming languages such as SQL and Python
Audience
AI/ML Developers, AI Practitioners, ML Engineers, Data Scientists
Available languages
English ، español (Latinoamérica) ، français ، bahasa Indonesia ، italiano ، 日本語 ، 한국어 ، polski ، português (Brasil) ، українська ، 简体中文 ، 繁體中文 ، Deutsch و Türkçe