Topic Modeling using PyCaret

ofrecido por
Coursera Project Network
En este Proyecto guiado, tú:

build an end-to-end Topic model using PyCaret

Learn how to evaluate a Topic Model

Clock2 hours
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

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.

Habilidades que desarrollarás

Topic ModelPython ProgrammingMachine LearningPyCaret

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  1. Introduction and setup environment

  2. Load and Prepare Data

  3. Explore Data

  4. Preprocess Data

  5. Build Topic Model

  6. Evaluate Model

  7. Deploy Model

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

Preguntas Frecuentes

Preguntas Frecuentes

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