IA explicable para todos

Resumen

This program explores how Explainable AI (XAI) enables practitioners to understand, interpret, and communicate machine learning model behavior with clarity and confidence. You’ll begin by learning the foundational principles of explainability, including interpretability, transparency, and the taxonomy of explanation methods.

Through hands-on activities, you will explore how different types of explanations apply to real-world models and how inherently interpretable models such as linear models and decision trees provide direct insight into model behavior. You’ll then dive into post-hoc explanation techniques that help interpret complex and black-box models.

You will learn the difference between model-agnostic and model-specific methods and apply techniques such as permutation importance, Partial Dependence Plots (PDP), and Individual Conditional Expectation (ICE) to analyze global feature effects. Practical demonstrations will guide you through implementing these methods, visualizing model behavior, and interpreting patterns that influence predictions.

Next, you’ll explore local explanation techniques, focusing on understanding individual predictions using LIME and SHAP. You will learn how surrogate models approximate local behavior and how Shapley values provide a theoretically grounded approach to feature attribution.

Hands-on exercises will help you generate and interpret both global and local SHAP insights, enabling deeper understanding of model decisions at multiple levels. Finally, you’ll examine the critical aspects of trust, fairness, and communication in Explainable AI.

You will learn how bias emerges in machine learning systems, how to evaluate fairness using practical tools, and how to balance accuracy with interpretability. You will also design clear and effective explanation reports, using visual and narrative techniques to communicate insights to both technical and non-technical stakeholders.

By the end of this program, you will be able to:

- Explain core Explainable AI concepts, including interpretability, transparency, and taxonomy - Interpret inherently interpretable models, including linear models and decision trees - Apply explanation techniques, including permutation importance, PDP, ICE, LIME, and SHAP - Evaluate model fairness, including bias detection and performance interpretability trade-offs - Design explanation reports, including clear and stakeholder-focused communication This program is designed for data scientists, machine learning engineers, AI practitioners, and analysts who want to build trustworthy and interpretable machine learning systems. A basic understanding of machine learning concepts and Python will help maximize your learning experience.

Learners need a reliable internet connection, a modern web browser, and access to standard machine learning tools and Python environments; no specialized hardware is required. Join us to master Explainable AI and learn how to interpret, evaluate, and communicate machine learning models with confidence and clarity.

Programa

• Fundamentos de la IA Explicable
Desarrolla una sólida base en IA Explicable aprendiendo a interpretar y analizar modelos de aprendizaje automático. Explora conceptos clave como interpretabilidad, transparencia y modelos inherentemente interpretables como la regresión lineal y los árboles de decisión. Aplica estos conceptos a través de ejercicios prácticos para entender el comportamiento de los modelos y sus aplicaciones en el mundo real.
• Técnicas de Explicación Post-Hoc
Explora cómo interpretar modelos complejos de caja negra utilizando técnicas de explicación post-hoc. Aplica métodos como la Importancia por Permutación, PDP, ICE, LIME y SHAP para analizar patrones globales y predicciones individuales. Adquiere experiencia práctica extrayendo conocimientos significativos de modelos del mundo real.
• Confianza, Sesgo y Comunicación
Construye sistemas de IA confiables y responsables abordando el sesgo, la equidad y la comunicación efectiva de los conocimientos del modelo. Evalúa la equidad del modelo, comprende los compromisos entre interpretabilidad y rendimiento, y aplica técnicas prácticas para detectar sesgos. Obtén experiencia práctica creando informes de explicaciones claros y centrados en las partes interesadas utilizando conocimientos de SHAP.
• Conclusión del Curso y Evaluaciones
Este módulo final evalúa tu comprensión de los conceptos de IA Explicable a través de la aplicación práctica. Interpreta modelos, aplica métodos de explicación global y local, y evalúa la equidad y el sesgo. Comunica conocimientos a través de informes claros, demostrando tu capacidad para construir sistemas de IA transparentes y confiables.

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Impartido por

Edureka

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Materias

Artificial Intelligence