Create your first Azure Machine Learning experiment

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This post will helps you to create your first Azure Machine Learning workspace. For this you should log up with either a free or paid Azure subscription, or using the free trial Azure Machine learning offer.

First open the Azure Management Portal https://manage.windowsazure.com, 
To create a new experiment you should click on +New in the bottom left corner of the screen, then you have a set of options to create : 
  • Data Set : this option allows you to upload a new dataset to use with your experiment from a local file on disk
  • Experiment: A Empty experiment or a preexisting experiment to help get you started fast
I would like to start with a real exemple such as predicting whether a person’s income exceeds $50,000 per year based on his demographics or census data. You can imagine how incredibly useful the ability to predict a person’s income might be in the world of sales and marketing.

This is exactly the kind of predictive analytics that would be most useful for a successful targeted marketing campaign for products that require buyers with a certain level of disposable income. This will be a simplified example of how you could use Azure Machine Learning with ML Studio and ML API web services to create a real-world, cloud-based predictive analytics solution to help drive a marketing campaign.

In this walkthrough, we follow the entire process of developing a predictive analytics model in Azure

Machine Learning Studio and then publish it as an Azure Machine Learning API web service.
We start by downloading a sample Census Income Dataset from a public repository such as the UCI Machine Learning Repository from the following link: http://archive.ics.uci.edu/ml/datasets/Census+Income.

We then develop and train a predictive model based on that dataset, and then publish the predictive model as a web service that can be used by other applications. These are the high-level steps we follow:

1. Download, prepare, and upload a census income dataset.

2. Create a new Azure Machine Learning experiment.

3. Train and evaluate a prediction model.

4. Publish the experiment as an Azure Machine Learning web service.

5. Access the Azure Machine Learning web service via sample tester programs.
in the next post i will explain how to implemente those steps

if you like to learn more about this concept go and visit :
https://gallery.azure.ai/experiments


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