There can be little doubt that the single hottest career in the data field is the data scientist or BI developer skilled in predictive analytics.
Yes, Big Data is on everyone’s lips but what happens after that big data is ingested into a data lake?
The answer is predictive analytics.
Because we live in the big data era, machine learning has become much more popular in the last few years.
Having lots of data to work with in many different areas lets the techniques of machine learning be applied to a broader set of problems.
Data can hold secrets, especially if you have lots of it.
With lots of data about something, you can examine that data in intelligent ways to find patterns.
This is exactly what machine learning does: It examines large amounts of data looking for patterns, then generates code that lets you recognize those patterns in new data.
Your applications can use this generated code to make better predictions. In other words, machine learning can help you create smarter applications.
Azure Machine Learning (Azure ML) is a cloud service that helps people execute the machine learning process.
As its name suggests, it runs on Microsoft Azure, a public cloud platform.
Because of this, Azure ML can work with very large amounts of data and be accessed from anywhere in the world. Using it requires just a web browser and an internet connection.
In this course you will be learning and building predictive algorithms using Azure Machine Learning Studio.
At the end of this course you’ll be able to build and evaluate a binary classification predictive model without authoring a single line of code
You’ll build an Experiment for a targeted email campaigned and be able to tell what customers should receive flyers and those that shouldn’t.
Thanks for reading about Azure Machine Learning Studio and I’ll see you in the course.
Is this course right for you?
I want to make sure you get the most out of this course so let's make sure you are in the right place.
In this lecture let's learn about the 4 pillars of analysis.
These are the very basics of analysis in data science.
Why should be use Azure Machine Learning Studio as our tool to craft our experiments?
Let's learn several compelling reasons why this product is a game changer for predictive analytics.
Why did big data and data science just become two of the hottest careers in the world.
A process approach to the data science process.
What steps do we need to take in order to begin modeling our data?
In this lecture let's learn some of the vernacular data scientist use.
Let's wrap up what's we've learned.
The cloud based environment where we build our predictive analytics experiments.
In the lecture let's navigate through the various panes and high level features.
In this lecture let's learn what an experiment is.
A confusion matrix is a table that is often used to describe the performance of a classification model (or "classifier") on a set of test data for which the true values are known.
In this lesson let's learn how to interpret the results of this matrix.
In this lesson we are going to define what Machine Learning is and talk about how it fits into AMLS.
This group of algorithms is widely used.
Let's talk about classification in this lecture.
In this lecture let's define what a use case is for building our classification model.
In this lecture let's learn why we are going to use a binary classification model.
Let's learn how to create our first experiment in Azure Machine Learning Studio.
We will step you through and end to end example on how to create a binary classification model.
On Part 2 let's finalize how to create our first experiment in Azure Machine Learning Studio.
In this lecture let's learn how to add another module to compare and contrast the results of our first model.
Let's learn some new vernacular in this lesson.
Let's summarize what we've learned in this section.
In this lecture we are going to learn what the client wants.
What are we trying to predict?
We are trying to predict people that will buy a bike by only targeting customers who have purchased one in the past.
In this lecture we are going to learn where the data came from.
We are going to export from SQL Server then import in Azure Machine Learning Studio.
I've provided the data set for this exercise in the download sections of the course.
In this lesson I just wanted to show where that data came from.
In this lecture we are going to build the core part of our experiment.
When this lecture is completed you'll have built a working Targeted Email Binary Classification Model.
In this lecture we will work through our error.
During the execution of our package something went wrong and we have to fix it.
Once our model has run successfully we need to know if it's ready for production or does it need some tweaking.
Our model is solid and in this lecture we will learn why.
In this brief we will learn how to score our model.
When our model ran two additional columns were created on our results.
Is this lecture we will learn why and what two columns we can look at to further evaluate our model.
Let's summarize what we've learned in this section.
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.