Build a Clustering Model using PyCaret

提供方
Coursera Project Network
在此指导项目中,您将:

build an end-to-end clustering model using PyCaret

Learn how to interpret a clustering model

Clock2 hours
Beginner初级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

In this 1-hour long project-based course, you will create an end-to-end clustering model using PyCaret a low-code Python open-source Machine Learning library. The goal is to build a model that can segment a wholesale customers based on their historical purchases. You will learn how to automate the major steps for building, evaluating, comparing and interpreting Machine Learning Models for clustering. 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. To be successful in this project, you should be familiar with Python and the basic concepts on Machine Learning.

您要培养的技能

  • Python Programming
  • Machine Learning
  • PyCaret
  • clustering

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  1. Introduction and setup of the environment

  2. Load and prepare the data

  3. Evaluate Model

  4. Preprocess Data

  5. Build Clustering Model

  6. Evaluate Model

  7. Interpret and Explain Model

  8. Deploy Model

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

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