Introduction To Azure Machine Learning Studio

Introduction

Beginning with this article, I am going to start writing a new series on Machine Learning using Azure Machine Learning Studio. Microsoft launched Azure Machine Learning Studio last year, for data analysis, predictive analysis, data mining, and data classification etc. Microsoft has already implemented most of the classic machine learning algorithms in Azure Machine Learning Studio. We don’t need to write our own data analysis algorithm if we use Azure ML Studio but still, there is opportunity for us to design our own data analysis algorithm using Python and R language.

This article is going to be an introduction of Azure ML Studio where I will talk about machine learning, Azure Machine Learning Studio subscription, data type, algorithm categories, and algorithms. Let’s talk about Azure Machine Learning Studio.

Machine Learning

Machine learning is a way to understand the data pattern, recognize it, analyze it, get insight of it, and predict accordingly for the future. It helps in data mining, language processing, image recognition, and Artificial Intelligence.

We can split machine learning into several areas - supervised learning, unsupervised learning, and recurrent learning. In this tutorial series, we will only talk about supervised and unsupervised learning.

Supervised learning is predicting the future by learning from past examples.

Unsupervised learning is understanding the past, making sense of data, learning the structure of data, and compressing data for consumption.

Azure Machine Learning Studio

Azure Machine Learning Studio is built considering the cloud-first mobile-first world.

This tool is fully managed and we don’t have a need to install any external software, or manage any hardware, as there is one portal to view and update everything.
It’s very simple to use. Simple drag, drop and connect interfaces are there to use for data science features. We don’t need any programming for common tasks.
33+ classic and mostly used data analysis algorithms are already implemented on this tool. We don’t need to implement new algorithm for basic use. But, it supports R and Python for extensibility usage.
We can create service and integrate our data analysis tool with our existing web, mobile, or desktop application very easily. We can monetize our designed data analysis model in machine learning marketplace as well.
We can collaborate work with anyone, anywhere via Azure workspace.
There is visual composition with end2end support for data science workflow.
There are built in immutable libraries of models. So, developers just need to search, discover, and reuse them according to their need.
As we don’t need to do implement our own code, we can test our dataset with different algorithms and models in few minutes.

Applications

Azure Machine Learning has endless possibilities. We can perform social network analysis, predictive maintenance, web app optimization, healthcare outcomes, natural resource exploration, fraud detection, churn analysis, life science research, network intrusion detection and many more.

Application Process

For any type of data analysis application, at first we need proper dataset. To develop a data analysis application for Azure Machine Learning Studio , we need to setup cloud environment. Azure Machine Learning Studio is there for us, it is integrated with our Azure cloud environment and it is fast, efficient, and very easy to use.

Azure Machine Learning Studio supports different types of data format. It supports,

ARFF
CSV
TSV
Excel
ZIP as data format.

We can use different data sources in Azure ML Studio. Some of the supported data sources are,

Azure Blob Storage
Azure SQL DB
Azure SQL DW
Azure Table
Hadoop Hive Query
OData Feed
Web URL(HTTP)
Desktop Direct Upload etc.

We can use different types of data preprocessing methods to explore and process our collected data. Some of them are,

Clean Missing Data
Clip Outliers
Edit Metadata
Different Filters
Feature Selection
Normalization of Data
Partition
Quantization etc.

We can use different methods of data visualization in Azure Machine Learning Studio. These data visualization models are already implemented. We just need to integrate in our system and use them. Some of them are,

Box Plots
Histograms
Bar Charts
Scatterplots
Python Plotting Libraries
ROC Graph
Decision Tree
Confusion Matrix

Implemented algorithms

On 8 major categories, 33+ classic and mostly used machine learning algorithms are already implemented in Azure Machine Learning Studio. Major categories and algorithms are,

Clustering

K-Means Clustering

Anomaly Detection

One-class support vector machine
PCA-Based Anomaly Detection
Time Series Anomaly Detection

Regression

Bayesian Linear Regression
Boosted Decision Tree Regression
Decision Forest Regression
Fast Forest Quantile Regression
Linear Regression
Neural Network Regression
Ordinal Regression
Poisson Regression

Two-Class Classification

Two-Class Averaged Perceptron
Two-Class Bayes Point Machine
Two-Class Boosted Decision Tree
Two-Class Decision Forest
Two-Class Decision Jungle
Two-Class Locally-Deep Support Vector Machine
Two-Class Logistic Regression
Two-Class Neural Network
Two-Class Support Vector Machine

Multi-Class Classification

Multiclass Decision Forest
Multiclass Decision Jungle
Multiclass Logistic Regression
Multiclass Neural Network
One-vs-All Multiclass

Computer Vision

OpenCV Library

Text Analytics

Feature Hashing
Named Entity Recognition
Vowpal Wabbit

Statistical Functions

Descriptive Statistics
Hypothesis Testing T-Test
Linear Correlation
Probability Function Evaluation

Account Creation

We don’t need any Azure Subscription, Credit card, to create Azure Machine Learning Studio account. We can chose anonymous guest access or sign in with our work, school or Microsoft account to use Azure ML Studio. But, if we have Azure subscription, then our Azure ML Studio account will automatically integrate with Azure account.

We can create Azure ML Studio account from this site, https-//studio.azureml.net/

Figure 1- Initial page of Azure Machine Learning Studio tool

From figure 1, we can see sign in and sign up option on https-//studio.azureml.net/

Figure 2- Azure Machine Learning Studio Dashboard

After signing up or signing in into the system, we can see the above dashboard page. Our existing experiments will show on the first page with a diagram at the right. On the left bar, we can see features and they are,

Projects
Experiments
Web Services
Note Books
Datasets
Trend Models
Settings

Figure 3- New Experiment Selection

There is a section at the bottom and if we click on the +New button at the bottom, then Figure 3 will appear. Here, we can select experiment templates from the Experiment section. Project and Notebook are still in preview mode. Notebook is for Python Jupyter notebook. We can create blank experiment from here.

Figure 4- Settings Page

From the Settings section, we can see my workspace name, ID, and workspace storage. For each guest user, Azure ML allocate 10 GB of data to use for free.

Figure 5- Existing Datasets

If we click on to Datasets option, then we can see that lots of standard datasets are already there for us to test our system and to learn about different models.

Figure 6- Existing Implemented Experiments

If we click on to Experiments section, then we can see that lots of experimental models are already there for us. We can search our suitable model, discover them and reuse them.

Figure 7- Structure of Sample Model

From the above figure, we can see Sample Model 3. If we click on the existing experimental models, then we can see each model's diagram like below. This model is the combination of different components.

Figure 8- Visualizing existing model data

If we click on the cross validate model and then right click on this component, then we can see options like the above figure. Click on the Visualize option to see the data.

Figure 9- Data visualization of existing model

After clicking on to Visualize data, we can see section like above image. This is the raw data and this model is designed to analyze that raw data.

Conclusion

This is the first article of a long series of articles on Machine Learning and Azure Machine Learning Studio. It’s just an introduction article of this tool. From the next article, we will pick each algorithm, will describe the algorithm in very easy words, and then develop a machine learning application using that application from the scratch.