Optimization of Topic Models using Grid Search Method

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12 個評分
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Coursera Project Network
在此指導項目中,您將:

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高級設置
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

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.

您要培養的技能

Topic Modelmodel optimizationHyperparameter OptimizationApplied Machine Learning

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  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

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

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