20 Responsible AI for Developers: Interpretability & Transparency

20

Responsible AI for Developers: Interpretability & Transparency

Name: Responsible AI for Developers: Interpretability & Transparency
Rating: 4.08 (3481 reviews)

3 heures Intermédiaire

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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info

Informations sur le cours

Objectifs

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

Prérequis

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

Cible

AI/ML Developers, AI Practitioners, ML Engineers, Data Scientists

Langues disponibles

English, español (Latinoamérica), français, bahasa Indonesia, italiano, 日本語, 한국어, polski, português (Brasil), українська, 简体中文, 繁體中文, Deutsch et Türkçe

20

Responsible AI for Developers: Interpretability & Transparency

20

Responsible AI for Developers: Interpretability & Transparency

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