Article: Text Classification in Azure Machine Learning using Word Vector

WEKA or Waikato Environment for Knowledge Analysis developed at the University of Waikato, New Zealand, is a good tool to perform text Information Retrieval as it has a lot of features like Term Frequency (TF). Inverse Document Frequency (IDF), NGram Tokenization, Stopwords, Stemming, Document Length. 

This latest article Text Classification in Azure Machine Learning using Word Vectors describes how the output of word vectors in weka can be used in Azure Machine learning in order to process better classification.

Following is the table of content for the article series on Azure Machine Learning.

Introduction to Azure Machine Learning using Azure ML Studio

Data Cleansing in Azure Machine Learning

Prediction in Azure Machine Learning

Feature Selection in Azure Machine Learning

Data Reduction Technique: Principal Component Analysis in Azure Machine Learning

Prediction with Regression in Azure Machine Learning

Prediction with Classification in Azure Machine Learning

Comparing models in Azure Machine Learning

Cross Validation in Azure Machine Learning

Clustering in Azure Machine Learning

Tune Model Hyperparameters for Azure Machine Learning models

Time Series Anomaly Detection in Azure Machine Learning

Designing Recommender Systems in Azure Machine Learning

Language Detection in Azure Machine Learning with basic Text Analytics Techniques

Azure Machine Learning: Named Entity Recognition in Text Analytics

Filter based Feature Selection in Text Analytics

Latent Dirichlet Allocation in Text Analytics

Recommender Systems for Customer Reviews

AutoML in Azure Machine Learning

AutoML in Azure Machine Learning for Regression and Time Series

Building Ensemble Classifiers in Azure Machine Learning

Text Classification in Azure Machine Learning using Word Vectors