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Explainable AI in Python

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

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4 hours14 videos42 exercises

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Course Description

Discover the Power of Explainable AI

Embark on a journey into the intriguing world of explainable AI and uncover the mysteries behind AI decision-making. Ideal for data scientists and ML practitioners, this course equips you with essential skills to interpret and elucidate AI model behaviors using Python, empowering you to build more transparent, trustworthy, and accountable AI systems. By mastering explainable AI, you'll enhance your ability to debug models, meet regulatory requirements, and build confidence in AI applications across diverse industries.

Explore Explainability Techniques

Start by understanding model-specific explainability approaches. Use Python's libraries like Scikit-learn to visualize decision trees and analyze feature impacts in linear models. Then, move to model-agnostic techniques that work across various models. Utilize tools like SHAP and LIME to offer detailed insights into overall model behavior and individual predictions, refining your ability to analyze and explain AI models in real-world applications.

Dive deeper into explainability

Learn to assess the reliability and consistency of explanations, understand the nuances of explaining unsupervised models, and explore the potential of explaining generative AI models through practical examples. By the end of the course, you'll have the knowledge and tools to confidently explain AI model decisions, ensuring transparency and trustworthiness in your AI applications.
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  1. 1

    Foundations of Explainable AI

    Free

    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.

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    Introduction to explainable AI
    50 xp
    Decision trees vs. neural networks
    100 xp
    Model-agnostic vs. model-specific explainability
    50 xp
    Explainability in linear models
    50 xp
    Computing feature impact with linear regression
    100 xp
    Computing feature impact with logistic regression
    100 xp
    Explainability in tree-based models
    50 xp
    Computing feature importance with decision trees
    100 xp
    Computing feature importance with random forests
    100 xp
  2. 3

    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.

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  3. 4

    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.

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datasets

Graduate admissionsHeart diseaseInsuranceIncome

collaborators

Collaborator's avatar
James Chapman
Collaborator's avatar
Jasmin Ludolf
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Alex Kuntz

audio recorded by

Fouad Trad's avatar
Fouad Trad
Fouad Trad HeadshotFouad Trad

Machine Learning Engineer

Fouad is an experienced ML engineer, researcher, and educator, currently pursuing a Ph.D. in applied ML, with a focus on cybersecurity applications. His talent lies in simplifying complex data science concepts, making them accessible to everyone.
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