Aplicar técnicas de IA y prescriptivas.

Resumen

Transform your analytical capabilities into competitive advantage with AI-powered decision intelligence. This Short Course was created to help data analysts accomplish strategic business impact through advanced AI techniques and prescriptive analytics.

By completing this course, you'll be able to build ensemble AI solutions that combine multiple methodologies, evaluate performance trade-offs across competing models, and implement optimization frameworks that drive measurable business outcomes. By the end of this course, you will be able to:

Apply ensemble AI techniques to solve defined business problems with documented rationale Evaluate accuracy, latency, and interpretability trade-offs across multiple AI approaches Implement linear programming optimization for product mix and profit maximization Create weighted-scoring models for prescriptive scenario evaluation This course is unique because it bridges the gap between technical AI implementation and strategic business decision-making, providing hands-on experience with real-world optimization challenges.

To be successful in this project, you should have a background in basic analytics, Python programming, and business problem-solving experience.

Programa

• Módulo 1: Técnicas de IA de Ensamble - Fundamentos
Los aprendices aplicarán un conjunto de técnicas de IA fundamentales, avanzadas y generativas para resolver un problema definido de decisión empresarial, documentando el razonamiento detrás de la selección del modelo.
• Módulo 2: Evaluación de Compromisos de Rendimiento - Aplicación Central
Los aprendices evaluarán los compromisos de rendimiento entre precisión, latencia e interpretabilidad de al menos tres técnicas de IA en el mismo conjunto de datos y recomendarán la opción óptima.
• Módulo 3: Optimización Prescriptiva - Integración y Evaluación
Los aprendices aplicarán la programación lineal para la optimización de decisiones de mezcla de productos y evaluarán escenarios prescriptivos competidores utilizando modelos de puntuación ponderada para la presentación a partes interesadas.

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Impartido por

Hurix Digital

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Materias

Data Science