Explainable AI (XAI) Specialization

Build Ethical and Transparent AI Systems. Master skills in explainability techniques and ethical AI development to create trustworthy and transparent machine learning solutions.

Instructor: Brinnae Bent, PhD

What you'll learn

  •   Implement XAI approaches to enhance transparency, trust, robustness, and ethics in decision-making processes.
  •   Build interpretable models in Python, including decision trees, regression models, and neural networks.
  •   
  •   Apply advanced techniques like LIME, SHAP, and explore explainability for LLMs and computer vision models.

    • Skills you'll gain

  •   Machine Learning
  •   Machine Learning Methods
  •   Artificial Intelligence
  •   Regression Analysis
  •   Applied Machine Learning
  •   Visualization (Computer Graphics)
  •   Deep Learning
  •   Generative AI
  •   Large Language Modeling
  •   Artificial Neural Networks
  •   Statistical Modeling
  •   Decision Tree Learning

    • Specialization - 3 course series

    Course 3 Projects: Advanced labs focus on local explanations using LIME, SHAP, and Anchors, along with visualizing saliency maps and Concept Activation Vectors using free platforms like Google Colab for GPU resources. The projects provided in this Specialization prepare learners to create transparent and ethical AI solutions for real-world challenges.

    Interpretable Machine Learning

    Explainable Machine Learning (XAI)

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