How to Become A Data Scientist Using Azure Machine Learning

A Practical Introduction To Microsoft's Azure Machine Learning Tools

Created byMike West

Last updated 12/2015

English

What you'll learn

Build an end to end Predictive Model In Azure Machine Learning Studio
You'll gain a high level background in data science.
You'll be able to effectively use Microsoft's AML service.

Course content

6 sections • 39 lectures • 1h 11m total length

Course Introduction1:32
Let's go over what we will cover in this course.
Is this Course Right For You?1:31
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.
Download Course Material Here0:15
What is Data Science?1:33
In this lecture let's define what data science really means.
Analytics Spectrum1:28
In this lecture let's learn about the 4 pillars of analysis.

These are the very basics of analysis in data science.
Why Use Azure Machine Learning Studio?0:33
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 Does It Matter Now?2:22
Why now?

Why did big data and data science just become two of the hottest careers in the world.
The High Level Data Science Process4:00
A process approach to the data science process.

What steps do we need to take in order to begin modeling our data?
Azure Algorithms1:31
Terminology0:24
In this lecture let's learn some of the vernacular data scientist use.
Summary0:32
Let's wrap up what's we've learned.
Quiz

Azure Machine Learning Studio5:16
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.
Components of an Experiment1:26
In this lecture let's learn what an experiment is.
Four Step Creation Process1:27
Confusion Matrix Overview3:08
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.
Terminology0:34
Summary1:05
Quiz

Use Case0:27
In this lecture let's define what a use case is for building our classification model.
Why A Binary Classification Model?0:26
In this lecture let's learn why we are going to use a binary classification model.
Creating The Experiment - Part 110:37
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.
Creating The Experiment - Part 23:42
On Part 2 let's finalize how to create our first experiment in Azure Machine Learning Studio.
Add a Second Algorithm to Compare Against Our First2:24
In this lecture let's learn how to add another module to compare and contrast the results of our first model.
Reading The Models Outcome0:41
Terminology0:36
Let's learn some new vernacular in this lesson.
Summary1:10
Let's summarize what we've learned in this section.
Quiz

The Business Use Case0:24
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.
Export The Dataset2:08
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.
Upload Data Set2:40
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.
Creating the Experiment - Part 15:44
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.
Creating the Experiment - Part 22:56
In this lecture we will work through our error.

During the execution of our package something went wrong and we have to fix it.
Analyze the Outcome1:17
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.
Scoring Our Model0:57
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.
Summary1:03
Let's summarize what we've learned in this section.
Quiz

Requirements

Basic data skills and statistics would be helpful but this is an entry level course.

Description

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.

Who this course is for:

This course is for developers, business analysts and any data professional who want to learn the foundation of data mining.

How to Become A Data Scientist Using Azure Machine Learning

What you'll learn

Explore related topics

Course content

An Introduction to Data Science11 lectures • 16min

Azure Machine Learning6 lectures • 13min

Introduction to Statistical and Machine Learning Algorithms5 lectures • 5min

Creating A Simple Binary Classification Model8 lectures • 20min

Building A Simple Targeted Marketing Campaign8 lectures • 17min

Conclusion1 lecture • 1min

Requirements

Description

Who this course is for: