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Categories Data Science

From Privacy to Fairness in AI

  • Learners should possess a fundamental knowledge of artificial intelligence concepts, including machine learning algorithms, data handling, and model evaluation.
  • A willingness to engage with complex concepts and explore advanced methodologies surrounding fairness, privacy-preserving techniques, and ethical considerations in AI is essential.
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What I will learn?

  • Understand various methodologies and frameworks aimed at safeguarding data privacy in AI.
  • Gain a profound understanding of ethical frameworks in AI, exploring the ethical implications of algorithms, bias mitigation strategies, and the socio-cultural impact of AI applications.
  • Learn to identify and address biases within AI models.

Course Curriculum

From Privacy to Fairness in AI: Video Lesson

  • Current challenges in business for ML/AI
    01:06:20
  • How to create fairness in AI?
    02:29:49
  • Introduction to Privacy-Preserving ML/AI
    02:08:41
  • Understanding the Ethics of AI
    23:11

From Privacy to Fairness in AI: Handout Notes

Hands-on Implementation

Assessment

Testimonials

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Material Includes

  • Video Lesson
  • Handout Notes
  • Python Codes
  • MCQs

Requirements

  • Learners should possess a fundamental knowledge of artificial intelligence concepts, including machine learning algorithms, data handling, and model evaluation.
  • A willingness to engage with complex concepts and explore advanced methodologies surrounding fairness, privacy-preserving techniques, and ethical considerations in AI is essential.

Who Should Take this course?

  • Intermediate in Data Science

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