PyCaret: Anatomy of Regression

ofrecido por
Coursera Project Network
En este Proyecto guiado gratis, tú:

How to create a regression environment and compare model performance

Create best performing regression models

Using hyper parameter to tune models

Demuestra esta experiencia práctica en una entrevista

Clock2 hours 15 mins
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

In this 2 hour and 15 mins long project-based course, you will learn how to ow to set up PyCaret Environment and become familiar with the variety of data preparing tasks done during setup, be able to create, see and compare the performance of several models, learn how to tune your model without doing an exhaustive search, create impressive visuals of models, interpret models with the wrapper around SHAP Library and much more & all this with just a few lines of code. Note: This project works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.


Familiar with regression models, Sklearn and Python

Habilidades que desarrollarás

PyCaretMachine LearningPython ProgrammingregressionAuto ML

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  1. Task 1: Import Data, Initial dataset check and setup Pycaret environment

  2. Task 2: Create regression environment and compare model performance

  3. Task 3: Create best performing regression models

  4. Task 4: Hyper Parameter tuning the models

  5. Task 5: Stacking & Ensemble

  6. Task 6: Visualize and interpret the machine learning model

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

Preguntas Frecuentes

Preguntas Frecuentes

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