Machine learning is one of the fields that having growing popularity from past decades probably had been there for long time as a different term which might be “data mining”. Microsoft ML studio in azure is quite easy to use and relatively cheap to use even in big projects.
What is Machine Learning?
According to Wikipedia Machine learning is the subfield of computer science that “gives computers the ability to learn without being explicitly programmed” (Arthur Samuel, 1959).
However I like to simply describe it as finds pattern in data and use those patterns to predict future!. E.g. Assume you had sample data set which contains income of particular individuals with some other various attributes about themselves such as social status, occupation, geographical are, age etc. Based on those sample data we can predict income of a individual given the other data attributes about that individual except income.
Using Microsoft Azure Machine Learning Studio without zero programming!
One cool thing about Azure ML studio is You can run lot of machine learning projects with zero programming (Rather than using R or Python scripts). Although it support Python and R scripts if necessary in some scenarios but most common decision tree algorithms can run with zero programming!
A Sample case study in Microsoft Azure Machine learning Studio for Heart Disease Prediction.
There is a sample machine learning project that you can run in machine learning studio. Based on previous clinical diagnostic data of heart disease patients you can predict presence of heart disease in a person Cortana gallery contains the sample in here. To run this project in your azure environment you can do following.
Log into Microsoft machine learning studio
Navigate to https://gallery.cortanaintelligence.com/Experiment/Heart-Disease-Prediction-2 and click open in studio as shown below.
Project will be opened in Microsoft ML studio you can inspect how the workflow has been built and run the workflow
You can visualize scored model for positive and negative predictions(heart disease presence or absence in this case) with their underlying scored probability in scored model.