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Artificial Intelligence
Artificial Intelligence
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Grow with Google

Responsible AI for Developers: Interpretability & Transparency

  • up to 3 hours
  • Intermediate

This course introduces the concepts of AI interpretability and transparency, highlighting their importance for engineers and developers. Explore functional methods and tools to achieve interpretability and transparency in AI models and data.

  • AI interpretability
  • AI transparency
  • Machine learning tools

Overview

In this course, you will learn to define and describe the importance of interpretability and transparency in AI. You will explore various tools and techniques used to achieve these goals, gaining practical knowledge in machine learning concepts and practices. This course is designed to boost your cloud career by showcasing the skills you develop.

  • Web Streamline Icon: https://streamlinehq.com
    Online
    course location
  • Layers 1 Streamline Icon: https://streamlinehq.com
    Spanish
    course language
  • Professional Certification
    upon course completion
  • Self-paced
    course format
  • Pre-recorded classes
    delivered online

Who is this course for?

AI Engineers

Professionals looking to enhance their understanding of AI interpretability and transparency.

Developers

Developers interested in implementing transparent AI models in their projects.

Data Scientists

Data scientists aiming to improve their skills in AI model interpretability.

Why should you take this course?

Artificial Intelligence

Gain a competitive edge by mastering AI interpretability and transparency. This course covers essential tools and techniques, making it ideal for AI engineers, developers, and data scientists. Enhance your career by showcasing your expertise in transparent AI models.

Pre-Requisites

1 / 3

  • Practical knowledge of machine learning concepts and practices

  • Experience with machine learning pipelines and tools

  • Previous experience with programming languages like SQL and Python

What will you learn?

Introduction to AI Interpretability and Transparency
Learn the fundamental concepts of interpretability and transparency in AI, and understand their significance in the development of AI models.
Tools and Techniques for AI Interpretability
Explore various tools and techniques that help achieve interpretability in AI models, ensuring transparency in data handling and model predictions.

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