Building a Binary Classification Model in Azure ML
3.8 (2 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
28 students enrolled
Wishlisted Wishlist

Please confirm that you want to add Building a Binary Classification Model in Azure ML to your Wishlist.

Add to Wishlist

Building a Binary Classification Model in Azure ML

What's the probability you'd live or die on the Titanic?
3.8 (2 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
28 students enrolled
Created by Mike West
Last updated 2/2017
Curiosity Sale
Current price: $10 Original price: $20 Discount: 50% off
30-Day Money-Back Guarantee
  • 1 hour on-demand video
  • 3 Articles
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • You'll be able to use Azure ML to build a binary classification model from end to end.
  • You'll understand how to score and evaluate a binary classification model.
  • You'll use what you've learned to predict whether you would have lived or would have died if you were aboard the Titanic.
View Curriculum
  • A basic understanding of Azure Machine Learning.
  • A high level understanding of machine learning.

"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 significantly easier 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 most common 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 results so 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.
Students Who Viewed This Course Also Viewed
Curriculum For This Course
Course Introduction
4 Lectures 07:19

What are we going to learn in this course? 

Let's find out. 

Preview 01:20

What are we seeking to accomplish? 

What's the end goal? 

Preview 02:00

In supervised learning we need a data set. 

Let's get one in this lesson. 

Preview 03:30

Let's wrap up what we've learned in this brief section. 


5 questions
The Modeling Process
7 Lectures 23:11

Let's define our categorical values in this lecture. 

Defining Our Categorical Variables

Most of our columns make sense but there are a few that don't. 

Let's rename those in this lecture. 

Preview 05:50

We don't want any holes in our data set. 

Let's set age to the median value. 

Clean Missing Age Values

There are a few columns we don't need. 

Let's get rid of them. 

Remove Unnecessary Columns

Let's tell Azure ML what our target variable is. 

Preview 01:46

In this lecture let's build our model. 

Building the Model


5 questions
Analyzing The Results
7 Lectures 22:33

In this lecture let's add a second model for comparison. 

Adding a Second Model for Comparison

How did our model do? 

Is it real world ready? 

Analyzing the Results

Let's learn what cross validation is before we implement it in our next lesson. 

What is Cross Validation?

In this lesson let's learn how to cross validate our results. 

Cross Validating Our Results

Let's add some more trees to our model. 

Is more better? 

Tuning the Model

In this lecture let's publish our model to a web service so others can use it. 

Publish To Web Service


10 questions
About the Instructor
Mike West
4.1 Average rating
2,625 Reviews
43,383 Students
40 Courses
SQL Server and Machine Learning Evangelist

I've been a production SQL Server DBA most of my career.

I've worked with databases for over two decades. I've worked for or consulted with over 50 different companies as a full time employee or consultant. Fortune 500 as well as several small to mid-size companies. Some include: Georgia Pacific, SunTrust, Reed Construction Data, Building Systems Design, NetCertainty, The Home Shopping Network, SwingVote, Atlanta Gas and Light and Northrup Grumman.

Experience, education and passion

I learn something almost every day. I work with insanely smart people. I'm a voracious learner of all things SQL Server and I'm passionate about sharing what I've learned. My area of concentration is performance tuning. SQL Server is like an exotic sports car, it will run just fine in anyone's hands but put it in the hands of skilled tuner and it will perform like a race car.


Certifications are like college degrees, they are a great starting points to begin learning. I'm a Microsoft Certified Database Administrator (MCDBA), Microsoft Certified System Engineer (MCSE) and Microsoft Certified Trainer (MCT).


Born in Ohio, raised and educated in Pennsylvania, I currently reside in Atlanta with my wife and two children.