Practical Methodology and Ethics in AI

This course is part of Foundations of Neural Networks Specialization

Instructor: Zerotti Woods

What you'll learn

  •   Learners will gain hands-on experience training and debugging deep learning models while considering deployment challenges and best practices.
  •   Students will understand and evaluate ethical concerns in AI, including bias, fairness, and the societal impact of deploying neural networks.
  •   Learners will explore how to integrate structured probabilistic models with deep learning, reducing uncertainty and improving model decision-making.

    • Skills you'll gain

  •   Data Ethics
  •   Artificial Intelligence and Machine Learning (AI/ML)
  •   Machine Learning
  •   Artificial Intelligence
  •   Deep Learning
  •   Applied Machine Learning
  •   Social Studies
  •   Ethical Standards And Conduct
  •   Probability Distribution
  •   Data-Driven Decision-Making
  •   Debugging
  •   Information Privacy
  •   Unstructured Data

    • There are 4 modules in this course

    What sets this course apart is its balanced focus on technical mastery and responsible AI practices. You’ll learn to handle incomplete data, analyze peer presentations, and critically evaluate AI’s broader societal impact. Whether you’re a data scientist or an AI enthusiast, this course will provide a comprehensive foundation to develop impactful and ethical AI solutions.

    Practical Methodology

    Ethical Considerations

    Structured Probabilistic Models

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