Explainable AI in Python

Via DataCamp

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Overview

"Explainable AI in Python" is a hands-on course focused on demystifying the decision-making processes of machine learning models using Python-based tools. As AI systems increasingly influence critical business and societal decisions, explainability is essential for building trust, ensuring fairness, and maintaining compliance with regulations. This course introduces the fundamentals of Explainable AI (XAI), helping learners understand why models behave the way they do. Participants will learn to implement popular Python libraries such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic...

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.