Entrenar diferentes modelos como Xgboost, Catboost o random forest para predecir series temporales
Predecir datos futuros en base a series de tiempo
Entrenar modelos avanzados de Machine Learning para series temporales
En este proyecto aplicado y práctico aprenderás a entrenar modelos capaces de predecir series temporales. Para ello utilizaremos la librería de Pycaret con Python y entrenaremos modelos como: XGBoost, Catboost o Random forest. También aprenderemos a generar modelos más avanzados con lñas diferentes técnicas de ensamblado de modelos. Al finalizar este curso habrás aprendido a entrenar tus propios modelos y a aplicarlos en tus propios proyectos.
Time Series
Machine Learning
Xgboost
PyCaret
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Introducción a las series temporales
Clases de modelos de series de tiempo
Fundamentos de Pycaret
Series temporales univariantes. Pre-procesamiento
Ejercicio aplicado. Pre-procesamiento de datos
Series temporales univariantes. Entrenamiento del modelo
Ejercicio aplicado. Entrenamiento de un modelo para predecir series de tiempo
Series temporales univariantes. Evaluación del modelo
Ejercicio aplicado. Evaluación del modelo
Series temporales univariantes. Modelos avanzados
Series temporales múltiples. Pre-procesamiento
Ejercicio aplicado. Series temporales múltiples
Series temporales múltiples. Entrenamiento y evaluación del modelo
Ejercicio aplicado. Series temporales múltiples. Parte II
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
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At the top of the page, you can press on the experience level for this Guided Project to view any knowledge prerequisites. For every level of Guided Project, your instructor will walk you through step-by-step.
Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.
You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.
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