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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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.For Business
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Foundations of Explainable AI
FreeBegin 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.
Introduction to explainable AI50 xpDecision trees vs. neural networks100 xpModel-agnostic vs. model-specific explainability50 xpExplainability in linear models50 xpComputing feature impact with linear regression100 xpComputing feature impact with logistic regression100 xpExplainability in tree-based models50 xpComputing feature importance with decision trees100 xpComputing feature importance with random forests100 xp - 2
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.
Permutation importance50 xpPermutation importance for MLPClassifier100 xpCoefficients vs. permutation importance100 xpSHAP explainability50 xpFinding key medical charge predictors with SHAP100 xpFinding key heart disease predictors with SHAP100 xpSHAP kernel explainer50 xpKernel explainer for MLPRegressor100 xpKernel explainer for MLPClassifier100 xpSHAP vs. model-specific approaches100 xpVisualizing SHAP explainability50 xpFeature Importance plots for admissions analysis100 xpAnalyzing feature effects with beeswarm plots100 xpAssessing impact with partial dependence plots100 xp - 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.
Local explainability with SHAP50 xpGlobal vs. local explainability100 xpSHAP for explaining income levels100 xpLocal explainability with LIME50 xpInterpreting regressors locally100 xpInterpreting classifiers locally100 xpText and image explainability with LIME50 xpExplaining sentiment analysis predictions100 xpExplaining food image predictions100 xp - 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.
Explainability metrics50 xpEvaluating SHAP explanation consistency100 xpEvaluating faithfulness with LIME100 xpExplaining unsupervised models50 xpFeature impact on cluster quality100 xpFeature importance in clustering with ARI100 xpExplaining chat-based generative AI models50 xpChain-of-thought to discover reasoning100 xpSelf-consistency to assess confidence100 xpCongratulations50 xp
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audio recorded by
Fouad Trad
See MoreMachine 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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