Introduction

This article goes up with a deep walk on Azure Machine Learning covering the topics with lot many modules which head up with loading data in Azure machine learning, data preparation, Azure machine learning modeling, deploying the models, and about R scripts in Azure.

Welcome to Azure Machine Learning

Days were there when we were helping folks on developing software to automate things. The next era started about thinking how to solve the existing problems that we have in real-time of our life and as it went on, we came up with a great technology which started predicting with Artificial Intelligence to give up the solution for the problems, to respond back as a Bot, to keep your data secured, and to recognize as a Cognitive Service, etc.
One such emerging trend that we have today to give the solution using available datasets is “Machine Learning”. Machine Learning is one powerful predictive technology that has come up with solutions for existing problems and the result of the solution cannot be 100% perfect but the way that it predicts is moreover the right solution for the problem. The accuracy of the solution depends on how perfectly the datasets match the current problem scenario. Machine Learning is a common factor that a maximum of IT giants works on today, like Microsoft with Azure Machine Learning Studio, Google on Machine Learning solutions as Cloud Machine Learning Engine, etc.
So, that’s a wrap mixing up things together for a better start about Azure Machine Learning!! Here, my writing will be completely in Azure Machine Learning Studio.

Why Machine Learning in the Cloud?

Machine Learning can be executed in any environment either with the computation at on-premise or at cloud but the maximum of giants today who work to come up with a solution on Machine Learning work only with the cloud, namely it's Azure Machine Learning or Google Cloud Machine Learning Engine! So, thinking about this in detail, we get charged up with strong valid points that stand up certainly when we speak about Azure Machine Learning Services.
Here are the points which consider it.
Ref - https://gallery.cortanaintelligence.com/solutions

Why Machine Learning in Microsoft Azure?

“Microsoft & Machine Learning – Long in touch..”
Starting from the period of 1999, Microsoft started working on Machine Learning to help the users on computers to work with filtration at junk emails in inboxes, for example – Windows Live Hotmail. In 2004, Microsoft Search Engine “Bing” was built using Machine Learning followed up by 2005 for “Microsoft SQL Server” which enables the data mining for databases, by 2008 traffic prediction service was built on “Bing Maps” using azure machine learning, by 2010 users questers were predicted by Microsoft Kinect, by 2012 “Skype Translator” was built in for speech-to-speech translation using Azure Machine Learning, by 2014 Microsoft has come up with Microsoft Machine Learning to make innovations joining the hands of all common developers using Cortana Intelligence Gallery.

Start with Azure Machine Learning Service

“Data | Predictive Model | Operational Web API in minutes”
Azure Machine Learning Service can help to get the data from Blobs and Tables, Hadoop (HDInsight) from Relational Databases using Azure SQL Database and integrate them with the Machine Learning Development Environment using Azure Machine Learning Studio and make the model available for the client as a web app or as any forms of accessibility supporting all sort of devices like phones, tabs and PC’s.
The Machine Learning Studio from Microsoft Azure supports us with a visual editor for composing, testing, refining and deploying ML models which includes hundreds of modules, includes common algorithms for classification, regression and more. Azure ML studio also supports the number of input formats followed by the support for R and Python. Machine Learning provides many algorithms classified into groups as anomaly algorithms, classification, clustering, and regression.
azure-image
Anomaly detection is the identification of items, events, or observations which do not conform to an expected pattern or other items in a dataset. A classic example is examining a dataset representing banking transactions and detecting potentially fraudulent transactions in that group.
Regression algorithms seek to establish and quantify relationships between variables. By establishing a relationship between a dependent variable and one or more independent variables, regression analysis can enable the value of a dependent variable to be predicted given a set of inputs with a quantifiable accuracy. A great example can be seen at https://how-old.net/#, a site that lets you upload a photo and then guesses your age with uncanny accuracy. The site uses Azure Machine Learning and combines classic regression with advanced image recognition.
The purpose of classification algorithms is to identify the category to which an observation belongs based on training data consisting of observations that have already been classified (assigned to a category). A great example is determining whether an e-mail belongs to the "spam" category or the "not-spam" category.
Clustering seeks to group a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups (clusters).
Check for the Algorithm Cheatsheets over here - http://aka.ms/MLCheatSheet

Summary

Hope this writing would have helped you in understanding the basics of Machine Learning, why we should perform it on cloud and that too with Azure cloud with all possibilities that we have from Azure Machine Learning Studio classifying the algorithms.
Follow up my next writing on getting started with Azure Machine Learning Studio to work and a brief introduction to Azure Storage.