Explainable Artificial Intelligence (XAI) Concepts
Understand the role and real-world realities of Explainable Artificial Intelligence (XAI) with this beginner friendly course.
Comience El Curso Gratis1 hora12 vídeos36 ejercicios
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Descripción del curso
Understand the Core Concepts of Explainable Artificial Intelligence (XAI)
This course introduces the crucial field of XAI, focusing on making complex AI algorithms understandable and accessible. The need for transparency and trust in these technologies grows as AI systems become increasingly integrated into various sectors. This course covers the core concepts of XAI, including transparency, interpretability, and accountability, and explores the balance between model complexity and explainability.Learn XAI Techniques
You will learn about model-specific and model-agnostic explanations, gaining practical insights and tools to apply XAI principles effectively in your projects. The course aims to equip you with the knowledge to make AI systems more transparent, ethical, and aligned with societal values, ensuring that AI decisions are not only effective but also justifiable and understandable.Implement XAI in the Real World
By the end of this course, you will have a solid understanding of XAI and its importance in the development of AI solutions, and you will be ready to implement these principles to enhance the clarity and trustworthiness of AI systems in real-world applications.Empresas
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Obtenga acceso de su equipo a la biblioteca completa de DataCamp, con informes centralizados, tareas, proyectos y másEn las siguientes pistas
Liderazgo en Inteligencia Artificial (IA)
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Introduction To Explainable AI
GratuitoWe delve into Explainable AI (XAI), emphasizing its role in rendering AI systems transparent, interpretable, and trustworthy. We explore AI's capabilities in prediction and content generation, underscoring the necessity for clear decision-making processes. Additionally, we investigate methods to make complex AI models more comprehensible to a wide range of audiences.
Why Explainable AI matters50 xpTransparency or interpretability50 xpInterpretable scenarios50 xpXAI objectives50 xpThe technicalities of XAI50 xpUnderstanding the fundamentals of XAI100 xpAccuracy and interpretability50 xpCommunicating about XAI50 xpAudience segmentation50 xpBalancing accuracy and simplicity50 xp - 2
Techniques in Explainable AI
We explore Explainable AI (XAI) techniques, categorizing them into model-specific, model-agnostic, local, and global explanations to clarify AI decision-making. We discuss regression and classification for model-specific insights and introduce SHAP and LIME to interpret black box models. Additionally, we address the complexity of Large Language Models (LLMs), emphasizing the need for transparency in their decision-making processes.
XAI techniques50 xpLocal or global50 xpModel-specific or model-agnostic100 xpModel-specific explanations50 xpAnalogies for regression and classification50 xpBlack box models in decision making50 xpModel-agnostic explanations50 xpSHAP, LIME or Both?100 xpRole of SHAP and LIME in XAI50 xpExplainability of LLMs50 xpUnderstanding LLMs50 xpReasoning of LLMs50 xp - 3
Implementing and Applying XAI
We explore the transformative impact of XAI in making artificial intelligence more accessible and user-friendly across various sectors. By integrating explainability from the outset, we ensure AI systems are transparent, fostering trust and facilitating a deeper collaboration between humans and machines. Through real-world case studies, we highlight how XAI demystifies complex AI decisions, empowering users with diverse technical backgrounds to leverage AI insights for more informed decision-making.
Tailoring AI to the user50 xpXAI for the end-user50 xpRisks of not including the end-user50 xpXAI by design50 xpJob applicant screening50 xpApplying XAI by design100 xpXAI for online learning50 xpXAI in action50 xpXAI requirements per industry100 xpTrusting the AI system50 xpFuture of XAI50 xpCross disciplinary XAI50 xpProactive development and deployment50 xpRecap50 xp
Empresas
¿Entrenar a 2 o más personas?
Obtenga acceso de su equipo a la biblioteca completa de DataCamp, con informes centralizados, tareas, proyectos y másEn las siguientes pistas
Liderazgo en Inteligencia Artificial (IA)
Ir a la pistacolaboradores
Folkert Stijnman
Ver MásML Engineer
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