Optimization of Topic Models using Grid Search Method

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在此指导 项目中,您将:

Necessity for optimization of Topic Models

Grid Search Method for optimizing Topic Models

Evaluate a best fit model - Compare model parameters and goodness of model scores from basic model

Clock2 Hours
Advanced高级设置
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

In this 2-hour long project-based course, you will learn how to optimize a topic model to achieve best fit using Grid Search method. Topic modelling is an efficient unsupervised machine learning tool that aids in analyzing the latent themes from text datasets. But it is also necessary to learn to optimize the models to obtain the best fit model in order to achieve better interpretable themes to gain meaningful insights. In this project you will learn about the statistical parameters to gauge the model quality and create interactive visualization of the themes for a more intuitive evaluation of topic models. The focus of this project is primarily from an application point of view instead of underlying statistical mechanisms. 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.

您要培养的技能

Topic Modelmodel optimizationHyperparameter OptimizationApplied Machine Learning

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

  2. Clean dataset & Visualize frequent words

  3. Tokenization, Lemmatization and Word Document Matrix

  4. Build LDA Model with Scikit Learn

  5. Grid Search for Model Optimization

  6. Visualization of Top N-words of Best 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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