Free
Course

Learn Explainable AI (XAI)

Learn how to interpret the decision-making process of deep neural networks.

  • Skill level

    Beginner
  • Time to complete

    Approx. 1 hour
  • Certificate of completion

    Included with paid plans
  • Prerequisites

    None

About this course

The deep learning models that power AI systems are often black boxes. Explainable AI tries to understand how these models make decisions, so that we can use them responsibly. In this course, you will learn the basic techniques of Explainable AI, including Generative Adversarial Networks (GANs). You will also learn about legal rights to explanation, and the role of explanation in regulating AI.

Skills you'll gain

  • Understand how neural networks function

  • Evaluate common Explainable AI methods

  • Understand legal rights to explanation

Syllabus

The platform

4 out of 5 Codecademy learners achieve their goals

"Codecademy gave me the tools I needed to land my first job as a developer, completely changing my career from the waste industry." - Jonathan Manner, Sweden
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Meet the creator of the course
Ada Morse
Data Science Instructional Designer at Codecademy
Ada is a Data Science Instructional Designer at Codecademy. Her background is in mathematics, with a Ph.D. focused on the design of self-assembling DNA nanostructures. Ada has worked on courses across our Data Science catalog, covering topics including Python, Excel, and Data Engineering.

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    Codecademy Learner @ USA
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Unlock additional features with a paid plan

  • Practice Projects

    Guided projects that help you solidify the skills and concepts you're learning.
  • Assessments

    Auto-graded quizzes and immediate feedback help you reinforce your skills as you learn.
  • Certificate of Completion

    Earn a document to prove you've completed a course or path that you can share with your network.

Join over 50 million learners and start Learn Explainable AI (XAI) today!

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Frequently asked questions about Explainable AI (XAI)

  • Explainable AI refers to techniques for understanding how AI models make decisions under the hood.