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.
작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.
작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.
분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.