Development
Data Science
Microsoft Azure
How to Become A Data Scientist Using Azure Machine Learning
3.5 (222 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
1,159 students enrolled

How to Become A Data Scientist Using Azure Machine Learning

A Practical Introduction To Microsoft's Azure Machine Learning Tools
3.5 (222 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
1,159 students enrolled
Created by Mike West
Last updated 12/2015
English
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How to Become A Data Scientist Using Azure Machine Learning
Current price: $13.99 Original price: $19.99 Discount: 30% off
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This course includes
  • 1 hour on-demand video
  • 4 mins on-demand audio
  • 17 articles
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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
Expand all 39 lectures 01:11:40
+ – An Introduction to Data Science
11 lectures 15:42

Let's go over what we will cover in this course.

Course Introduction
Preview 01:32

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.

Is this Course Right For You?
Preview 01:31
Download Course Material Here
00:15

In this lecture let's define what data science really means.

What is Data Science?
Preview 01:33

In this lecture let's learn about the 4 pillars of analysis.

These are the very basics of analysis in data science.


Analytics Spectrum
Preview 01:28

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 Use Azure Machine Learning Studio?
00:33

Why now?

Why did big data and data science just become two of the hottest careers in the world.

Why Does It Matter Now?
Preview 02:22

A process approach to the data science process.

What steps do we need to take in order to begin modeling our data?

The High Level Data Science Process
Preview 04:00
Azure Algorithms
01:31

In this lecture let's learn some of the vernacular data scientist use.

Terminology
00:24

Let's wrap up what's we've learned.

Summary
00:32
Quiz
10 questions
+ – Azure Machine Learning
6 lectures 12:56

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.

Azure Machine Learning Studio
05:16

In this lecture let's learn what an experiment is.

Components of an Experiment
01:26
Four Step Creation Process
01:27

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.

Confusion Matrix Overview
03:08
Terminology
00:34
Summary
01:05
Quiz
10 questions
+ – Introduction to Statistical and Machine Learning Algorithms
5 lectures 05:05

In this lesson we are going to define what Machine Learning is and talk about how it fits into AMLS.

Machine Learning and Supervision
01:41
Anomaly Detection
00:53

This group of algorithms is widely used.

Let's talk about classification in this lecture.

Classification
01:42
Terminology
00:30
Summary
00:19
Quiz
10 questions
+ – Creating A Simple Binary Classification Model
8 lectures 20:05

In this lecture let's define what a use case is for building our classification model.

Use Case
00:27

In this lecture let's learn why we are going to use a binary classification model.

Why A Binary Classification Model?
00:26

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 1
10:37

On Part 2 let's finalize how to create our first experiment in Azure Machine Learning Studio.

Creating The Experiment - Part 2
03:42

In this lecture let's learn how to add another module to compare and contrast the results of our first model.

Add a Second Algorithm to Compare Against Our First
02:24
Reading The Models Outcome
00:41

Let's learn some new vernacular in this lesson.

Terminology
00:36

Let's summarize what we've learned in this section.

Summary
01:10
Quiz
10 questions
+ – Building A Simple Targeted Marketing Campaign
8 lectures 17:10

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.

The Business Use Case
00:24

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.

Export The Dataset
02:08

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.

Upload Data Set
02:40

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 1
05:44

In this lecture we will work through our error.

During the execution of our package something went wrong and we have to fix it.

Creating the Experiment - Part 2
02:56

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.

Analyze the Outcome
01:17

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.

Scoring Our Model
00:57

Let's summarize what we've learned in this section.

Summary
01:03
Quiz
10 questions
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.