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Building a Binary Classification Model in Azure ML

21 Mar , 2018  

Requirements
  • A basic understanding of Azure Machine Learning.
  • A high level understanding of machine learning.
 
Description

“First impressions are “Finally, a practicing educator” Course delivery is smooth and spot on. Right before you lose hope a gem like this pops up – thanks.”  – Don Councill

Welcome to Building a Binary Classification Model in Azure ML.

Microsoft’s goal of democratizing machine learning is taking shape.

Taking predictive analytics to public cloud seems like the next logical step towards large-scale consumerization of Machine Learning. Azure ML does just that, while making it significantlyeasier for the developers to build high probability machine learning models without a PhD in statistics.

In this course, we are going to build one of the simplest and mostcommon models, the binary classification model.

The goal of binary classification is to categorize data points into one of two buckets: 0 or 1, true or false and to survive or not to survive.

Many decisions in life are binary, answered either Yes or No. Many business problems also have binary answers. For example: “Is this transaction fraudulent?”, “Is this customer going to buy that product?”, or “Is this user going to churn?” In machine learning, this is called a binary classification problem.

We will use binary classification to predict the probability of someone surviving if they had been aboard the Titanic.

We are going to create an end to end workflow. We will download the data set, clean it, model it, evaluate it then publish our resultsso others can use it.

Upon completing the course you’ll know how to create a model that accurately predicts the survivability of an individual based on attributes in the data set.

You’ll gain insight into the vernacular used in machine learning. 

For example, in the last sentence I used the world ‘attribute.’  An attribute in machine learning is no different than a column in a data set.

Various attributes affect the outcome of the prediction. For example, my chance of survival was 21.07% if I would have been in first class. If I would have been in second class my changes dropped to 2.16%. Either way, I wouldn’t have made it. 

Thanks for your interest in Building a Binary Classification Model in Azure ML..  We will see you in the course!!!

Who is the target audience?
  • If you want to make the jump from developer, DBA or Data Analyst to Data Scientist then this course is for you.
  • This course is for those who are learning machine learning on the Azure ML Platform.
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