Optimization of Topic Models using Grid Search Method

4.1
별점
8개의 평가
제공자:
Coursera Project Network
학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

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
Advanced고등
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

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.

개발할 기술

Topic Modelmodel optimizationHyperparameter OptimizationApplied Machine Learning

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  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

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

자주 묻는 질문

자주 묻는 질문

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