Introducción a los algoritmos de regresión


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In this Guided Project, you will:

Analizar datos y generar modelos de regresión lineal y logística con Python

Encontrar los mejores parámetros para generar modelos óptimos

55 minutos
No download needed
Split-screen video
Desktop only

Al completar este proyecto de 1 hora de duración, entenderás y podrás desarrollar tus propios modelos de regresión (lineal y logístico) a partir de un conjunto de datos definidos, y optimizar los algoritmos de forma automática para encontrar los mejores parámetros para tus modelos. También podrás entender los pasos necesarios antes de diseñar tus modelos, como analizar tus datos y hacer limpiezas de acuerdo a los tipos de datos y caso de uso.

Skills you will develop

  • Python Libraries

  • Logistic Regression

  • Linear Regression

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Explorar y analizar los conjuntos de datos

  2. Usar algoritmo de regresión lineal simple

  3. Usar algoritmo de regresión lineal múltiple

  4. Usar algoritmo de regresión logística

  5. Ajustar los hiperparámetros del algoritmo

  6. Seleccionar el mejor modelo

How Guided Projects work

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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Frequently Asked Questions

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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.