Topic Modeling using PyCaret

In this 1-hour long project-based course, you will create an end-to-end Topic model using PyCaret a low-code Python open-source Machine Learning library. You will learn how to automate the major steps for preprocessing, building, evaluating and deploying Machine Learning Models for Topic . 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.

• Project Overview
• By the end of this project, you will create an end-to-end topic model using PyCaret a low-code Python open-source Machine Learning library. The goal is to build a model that can detect topics from Wikipedia users comments. You will learn how to automate the major steps for preprocessing, building, evaluating and deploying Topic Model.