Module 1: Course Overview

This module explains how the class will be structured and introduces course materials and additional administrative information.

Lessons
Introduction
Course Materials
Facilities
Prerequisites
What We’ll Be Discussing

Lab 1: Course Overview
Creating Your Azure Machine Learning Account

After completing this module, students will be able to:
• Successfully log into their virtual machine.
• Have a full understanding of what the course intends to cover.

Module 2: What is Machine Learning?
In this module, we will explain machine learning and the concepts behind it.

Lessons
Introduction
One Methodology
Supervised vs. Unsupervised Methods
Analytics Spectrum
Development Methodology with Azure Machine Learning Studio
Be Very Vigilant

After completing this module, students will be able to:
• Understand what machine learning is.
• Understand the differences between supervised and unsupervised methods.
• Understand the analytics spectrum.
• Understand the development methodology.

Module 3: Introduction to Azure Machine Learning Studio

In this module, we will explore the Azure Machine Learning Studio interface and walk through the options available.

Lessons
Experiments
Web Services
Notebooks
Datasets
Trained Models
Settings
Walkthrough Exercise and Group Discussions

Lab 1: Introduction to Azure Machine Learning Studio

Group Walkthrough Exercise and Discussion: Introduction to Azure Machine Learning Studio
Individual Exercise: Introduction to Azure Machine Learning Studio

After completing this module, students will be able to:
• Understand and utilize the Azure Machine Learning Studio interface.
Module 4: Data Preparation

In this module, we will cover the steps necessary for data cleaning and explore other data preparation techniques.

Lessons
Tools for Cleaning
Text Files vs. Binary Files
Structures of Data
Steps for Data Cleaning
Common Cleaning Tasks
Feature Selection Feature
Engineering Group
Discussion

Lab 1: Data Preparation

Group Exercise: Statistical Visualizations
Individual Exercise: Remove Duplicate Rows
Individual Exercise: Clipping Outliers
Individual Exercise: Feature Imbalance
Individual Exercise: Feature Selection

After completing this module, students will be able to:

• Understand and utilize tools for cleaning.
• Understand the differences between text files and binary files.
• Understand structures of data.
• Understand and utilize steps for data cleaning.
• Understand and utilize feature selection.
• Understand feature engineering.

Module 5: Machine Learning

Algorithms
In this module, we will explain the different types of algorithms available and their uses.

Lessons
Regression
Classification
Clustering
Anomaly Detection
Azure Machine Learning Cheat Sheet
Visualizations
Group Discussion and Exercises

Lab 1: Machine Learning

Algorithms
Group Exercise: Azure Machine Learning Cheat Sheet
Group Exercise: Binary Classification Model
Group Exercise: Split Data
Group Exercise: Unbalanced Datasets
Group Exercise: Classification Using Multivariate
Group Exercise: Visualize a Clustering Model

After completing this module, students will be able to:

• Understand and utilize regression.
• Understand and utilize classification.
• Understand and utilize clustering.
• Understand anomaly detection.
• Understand and utilize the Azure Machine Learning Cheat Sheet.
• Understand and utilize visualizations.

Module 6: Building Models

Exercises

In this module, we explore the topic of customer propensity (inclinations and tendencies) and how to use Machine Learning to help with this common business question. This is an exercise module which contains both instructor-led and individual exercises.

Lessons

Group Discussion 1: Data Acquisition
Group Discussion 2: Data Preparation
Group Discussion 3: Feature Selection
Group Discussion 4: Train Data
Group Discussion 5: Cross Validation and Comparing Regressions
Group Discussion 6: Results
Group Discussion: Evaluate the Solutions – Learn from Examples

Lab 1: Building Models – Exercises

Group Exercise and Discussion: Data Acquisition
Group Exercise and Discussion: Data Preparation
Group Exercise and Discussion: Feature Selection
Group Exercise and Discussion: Train Data
Group Exercise and Discussion: Cross Validation and Comparing Regressions
Individual Exercise: Regression
Individual Exercise: Classification