Este curso tiene como objetivo proporcionar a los estudiantes una comprensión integral de los modelos predictivos utilizando técnicas de Machine Learning. Los participantes aprenderán a implementar, evaluar y mejorar modelos predictivos aplicables a diversas problemáticas reales, utilizando herramientas como Python y bibliotecas especializadas.
Modelos predictivos con Machine Learning
Gain insight into a topic and learn the fundamentals.
Intermediate level
Some related experience required
99 hours to complete
3 weeks at 33 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Aprenderás Machine Learning en robótica, construirás modelos de regresión y clasificación, optimizarás y harás predicciones.
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Recently updated!
February 2025
Assessments
31 assignments
Taught in Spanish
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There are 4 modules in this course
En esta sección, los estudiantes instalarán y configurarán el software necesario para el curso, incluyendo Python y Jupyter Notebook. Se introducirán a los conceptos fundamentales del modelado de datos y los diferentes tipos de modelos predictivos. Además, aprenderán sobre los fundamentos de Machine Learning y comenzarán a construir su primer modelo predictivo básico.
What's included
13 videos14 readings7 assignments
Esta sección se enfoca en los modelos de regresión y clasificación, cubriendo desde la teoría hasta la práctica de la regresión lineal, múltiple y polinomial, así como las máquinas de soporte vectorial y los árboles de regresión. Los estudiantes también aprenderán sobre la regresión logística y la matriz de confusión para la evaluación de modelos clasificadores.
What's included
15 videos14 readings11 assignments
En esta sección, los estudiantes aprenderán a analizar la calidad de sus modelos mediante el uso de curvas ROC y otras métricas de evaluación. También se enfocarán en la creación de modelos robustos utilizando variables dummy y seleccionando características relevantes. Finalmente, se introducirán a métodos avanzados de regresión y técnicas de ensamble para mejorar el rendimiento de los modelos.
What's included
9 videos9 readings7 assignments
Esta sección aborda técnicas de agrupamiento, comenzando con la preparación de datos y el uso de K-means. También cubre el análisis de series de tiempo, incluyendo la predicción de valores futuros en diferentes contextos, como la bolsa de valores.
What's included
9 videos14 readings6 assignments
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