Article : Building Ensemble Classifiers in Azure Machine Learning

A new article of the series, Building Ensemble Classifiers in Azure Machine Learning that discusses how to combine multiple classifiers. 

In ensemble Classifiers, we will look at how to perform predictions using multiple classification techniques so that it can produce better models with higher accuracy or they can avoid overfitting. This is equivalent to a patient that is referring multiple specialist doctors to diagnosis a disease rather than relies on one doctor.

The complete experiment can be found at Ensemble Classification | Azure AI Gallery 

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

Article: AutoML in Azure Machine Learning Service Regression and Time Series

Having discussed the AutoML features in Azure Machine Learning, the latest article of the series is to discuss the AutoML for Regression and Time Series in Azure Machine Learning. 

Following are the other list of the articles in the Series. 

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

Article: AutoML in Azure Machine Learning Service

AutoML is a new concept in Machine Learning where most of the steps are carried by the AutoML process. Following are the typical steps that are processed by the standard machine learning process. 

In the AutoML process, when the data is accepted all the other processes such as Data preparation, feature engineering, model tunning etc are done automatically, AutoML in Azure Machine Learning is the latest article in the Azure Machine Learning article series.  

Following are the other list of the articles in the Series. 

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

 

Article: Recommender Systems for Customer Reviews

We discussed in a previous blog post, how test data is used for recommendation with an example of books.  Having discussed how to use recommender systems, now it is time to discussed How to perform Recommender systems user customer reviews using Azure Machine Learning in this latest article. 

Following is the final output for the recommender system where every user is provided with different products using customer reviews using LDA Technique. 

Azure Experiment is available here.

This is the 18th article of the series and you can find the previous articles from the following list. 

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

Webinar: Building Machine Learning Models With Azure Machine Learning

Join me on this Wednesday to discuss Basics of Building Machine Learning Models with Azure Machine Learning. 

Register At:     https://bit.ly/361PdOt

Data Analysis for Singlish Texts

Like many other non-English nations, we Sri Lankans used to type Sinhala words using English text. Though there are many word processing tools and apps are available for Sinhala texts, still, we see a lot of people use Singlish words. 

Not only these Singlish texts are difficult to read, at the research level there are difficulties in identifying these words.

In every text related research, the first task would be identifying the Language. We have discussed how to detect a language using Azure machine learning in a previous article. 

This post is to look at whether we can detect Singlish text using Azure Machine Learning. The following is the configured Azure Machine Learning experiment.

You can download the experiment from the Azure AI gallery. Let us look at some important findings in this experiment. 

Out of the 1400+ texts, 35% were identified as English may be due to the fact that letters are in English. Then the big surprise is much Singlish texts were identified as Indonesian and Romanian and the percentages are 26%, 13%. Not sure there is a relationship between the Singlish language with Indonesian and Romanian languages. 

Another important finding is that Singlish texts are identified as 40 different languages such as Maly, Turkish, Polish, Irish etc. 

Azure Machine Learning: Named Entity Recognition in Text Analytics

 

Recognition of Entities in Text Analytics is an important process in Text Analytics in order to find People, Places and Organizations. Named Entity Recognition in Azure Machine Learning is used to identify the name of entities such as people, locations, and organizations, etc. The Named Entity Recognition control will provide where the particular entity exists as well as this technique will help us to understand the context of a text.

The latest article explains how to recognize Named Entities in Azure Machine Learning. Following are the rest of the articles in the series.

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 AutoML

As you are aware, Machine Learning is not a simple task. It has many tasks such as Feature Engineering, Feature Selection, Algorithm Selection, Hyper Tune Parameters, Evaluation etc. Due to these many complexities in the Machine Learning process, machine learning is still a little far away from people who not excellent at Mathematics and Statistics. For example, you are looking for a classification problem, there many algorithms that you can choose such as Decision Trees, Random Forest, Naive Bayes, SVM, ANN etc. Sometimes, you may have to perform this process multiple iterations in order to achieve better results as shown in the following figure. 

source: https://towardsdatascience.com/understanding-feature-engineering-part-1-continuous-numeric-data-da4e47099a7b

AutoML or Automated ML or Automated Machine Learning provides, you to submit dataset provide you with a better algorithm and relevant parameters. There are many AutoML frameworks available. Microsoft too has an AutoML framework that is available in the Azure Machine Learning Platform.

By following this link, you can watch videos, research papers on AutoMLdone by Microsoft Research. 

https://www.microsoft.com/en-us/research/project/automl/

Experiments in Azure Machine Learning

Following are all the experiments that are built by me in Azure Machine Learning. 

Time Series Anomaly Detection

Recommender System for Restaurant

Recommender System with live data

Latent Dirichlet Allocation

Recommender System for Customer Review Data

Language Detection 

Named Entity Recognition - News

Anomaly Detection of Sri Lanka Paddy Production

How to Build Recommender Systems with Customer Review Data

Text Analytics is a complex and challenging task due to the fact that it has a variety of data.  In a previous blog post, we looked at how recommender systems were used by Amazon to improve sales of one book. 

This Azure Machine Learning Experiment shows to build Recommender systems for customer Review Data. 

References for Datasets

https://jmcauley.ucsd.edu/data/amazon/

Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering R. He, J. McAuley WWW, 2016 pdf

Image-based recommendations on styles and substitutes J. McAuley, C. Targett, J. Shi, A. van den Hengel SIGIR, 2015 pdf

Language Detection in Azure Machine Learning and Other Basic Text Analytics Techniques

 

Language Detection is an important process in Text Analytics. If you are dealing with Web data as your source, the first thing you need to filter out relevant language. Since every language has its own properties, it is important to filter the necessary langue. 

In Azure Machine Learning, there is a special control named Language Control. A new article in the Azure Machine Learning series discusses the language detection options. 

This is the 13th article of the series and the following are the previous articles. 

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

Latent Dirichlet Allocation in Azure Machine Learning

Latent Dirichlet Allocation (LDA) is a Topic modelling technique that used in Text Mining. This technique was first introduced in 2003 by this research paper. 

LDA can be achieved in the Azure Machine Learning platform as it has specific LDA control. This is the experiment that was created in the Gallery with more than 50 Azure Machine Learning controls. 

Designing Recommender Systems in Azure Machine Learning

Recommender systems are one of the very common applications in Machine Learning. We have previously discussed the cheat sheet for the recommender system in a previous blog post. Further, we discussed how Amazon has used a recommender system to increase sales of published books.

This is the 13th article in the Azure Machine Learning series which is on the Recommender Systems. In this article, the Hybrid recommender technique called MatchBox Recommender is used. In this article, Live data from Azure SQL Database was used in order to showcase the features of Azure Machine Learning.

The previous articles are listed below. 

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

  

Time Series Anomaly Detection in Azure Machine Learning

We were discussing different Time Series Anomaly Detection before with few posts. This is the article in the Azure Machine Learning article series. 

This article is the 12th article in the series and the previous articles are below. 

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

Tune Model Hyperparameters for Azure Machine Learning models

In the article series on Azure Machine Learning, the next article on Tune Model Hyperparameters for Azure Machine Learning models is published. Since there can be different model parameters, tune model hyperparameters will derive the best parameters to improve the model accuracy.

This article is the 11th article in the series and the previous articles are below. 

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