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DataCamp

Explainable AI in Python

via DataCamp

Overview

Gain the essential skills using Scikit-learn, SHAP, and LIME to test and build transparent, trustworthy, and accountable AI systems.

Explainable AI is critical for data scientists and machine learning practitioners. This course will equip you with the skills to interpret AI model behaviors using Python. You'll use Python's Scikit-learn library and tools like SHAP and LIME to visualize and gain insights on model behavior. By the end of the course, you'll be able to build more transparent, trustworthy, and accountable AI systems.

Syllabus

  • Foundations of Explainable AI
    • Begin your journey by exploring the foundational concepts of explainable AI. Learn how to extract decision rules from decision trees. Derive and visualize feature importance using linear and tree-based models to gain insights into how these models make predictions, enabling more transparent decision-making.
  • Model-Agnostic Explainability
    • Unlock the power of model-agnostic techniques to discern feature influence across various models. Employ permutation importance and SHAP values to analyze how features impact model behavior. Explore SHAP visualization tools to make explainability concepts more comprehensible.
  • Local Explainability
    • Dive into local explainability, and explain individual predictions. Learn to leverage SHAP for local explainability. Master LIME to reveal the specific factors influencing single outcomes, whether through textual, tabular, or image data.
  • Advanced topics in explainable AI
    • Explore advanced topics in explainable AI by assessing model behaviors and the effectiveness of explanation methods. Gain proficiency in evaluating the consistency and faithfulness of explanations, delve into unsupervised model analysis, and learn to clarify the reasoning processes of generative AI models like ChatGPT. Equip yourself with techniques to measure and enhance explainability in complex AI systems.

Taught by

Fouad Trad

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