Build a Classification Model using PyCaret

Offered By
Coursera Project Network
In this Guided Project, you will:

build an end-to-end classification model using PyCaret

Learn how to interpret a classification model

Clock2 hours
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this 1-hour long project-based course, you will create an end-to-end classification model using PyCaret a low-code Python open-source Machine Learning library. The goal is to build a model that can accurately predict whether a teacher's project proposal was accepted, based on the data they provided in their application. You will learn how to automate the major steps for building, evaluating, comparing and interpreting Machine Learning Models for classification. 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 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.

Skills you will develop

Python ProgrammingMachine LearningclassificationPyCaret

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. Introduction and setup of the environment

  2. Load and prepare the data

  3. Prepare text data

  4. Build Classification Model

  5. Evaluate Model

  6. Interpret the final Model

  7. Deploy Model

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

Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.