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    21

    Responsible AI for Developers: Interpretability & Transparency

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    21

    Responsible AI for Developers: Interpretability & Transparency

    3 hours Intermediate universal_currency_alt 5 Credits

    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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    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, and Türkçe
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