Build a Regression Model using PyCaret

von
Coursera Project Network
In diesem angeleitetes Projekt werden Sie:

build an end-to-end Regression model using PyCaret

Learn how to interpret a Regression model

Clock2 hours
BeginnerAnfänger
CloudKein Download erforderlich
VideoVideo auf geteiltem Bildschirm
Comment DotsEnglisch
LaptopNur Desktop

In this 1-hour long project-based course, you will create an end-to-end Regression model using PyCaret a low-code Python open-source Machine Learning library. The goal is to build a model that can accurately predict the strength of concrete based on several fatures. You will learn how to automate the major steps for building, evaluating, comparing and interpreting Machine Learning Models for regression. Here are the main steps you will go through: frame the problem, get and prepare the data, discover and visualize the data, create the transformation pipeline, build, evaluate, interpret and deploy the model. This guided project is for seasoned Data Scientists who want to build a accelerate the efficiency in building POC and experiments by using a low-code library. It is also for Citizen data Scientists (professionals working with data) by using the low-code library PyCaret to add machine learning models to the analytics toolkit In order to be successful in this project, you should be familiar with Python and the basic concepts on Machine Learning Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Kompetenzen, die Sie erwerben werden

  • Python Programming
  • Machine Learning
  • PyCaret
  • regression

Schritt für Schritt lernen

In einem Video, das auf einer Hälfte Ihres Arbeitsbereichs abgespielt wird, führt Sie Ihr Dozent durch diese Schritte:

  1. Introduction and setup of the environnement

  2. Load and Prepare Data

  3. Explore Data

  4. Preprocess Data

  5. Build Regression Model

  6. Evaluate Model

  7. Interpret and Explain the final Model

  8. Deploy Model

Ablauf angeleiteter Projekte

Ihr Arbeitsbereich ist ein Cloud-Desktop direkt in Ihrem Browser, kein Download erforderlich

Ihr Dozent leitet Sie in einem Video mit geteiltem Bildschirm Schritt für Schritt an.

Häufig gestellte Fragen

Häufig gestellte Fragen

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