Course Overview
Duration: 2 Days
This course is designed to introduce the participant to the exciting world of predictive analytics built using drag-and-drop with Microsoft Azure Machine Learning Studio, all without coding from your desktop, using your browser.

Who Should Attend
The course is targeted towards business analysts, business intelligence developers, and managers interesting in exploring the world of predictive analytics for use as a competitive tool.

Course Certifications
This course is part of the following Certifications:

Prerequisites
Before attending this course, students must have:

Working knowledge of their own business data.

Course Objectives
After completing this course, 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.

Understand and utilize the Azure Machine Learning Studio interface.

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.

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.

Understand data acquisition.

Understand data preparation.

Understand feature selection.

Understand and utilize Train Data.

Understand cross validation and comparing regressions.

Evaluate solutions and learn from examples.

Understand and utilize regression algorithms.

Understand and utilize classification algorithms.

Understand and utilize clustering algorithms.

Understand utilize joined datasets.

Understand and utilize Power BI.

Course Content
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 : 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

Lab : What is Machine Learning?

None

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 : 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 : 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 : 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: Evaluation the Solutions – Learn from Examples

Lab: Building Models – Exercises

Group Exercise and Discussion: Data Acquisition

Group Exercise and Disccussion: 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

Individual Exercise: Clustering

After completing this module, students will be able to:

Understand data acquisition

Understand data preparation

Understand feature selection

Understand and utilize Train Data

Understand cross-validation and comparing regressions

Evaluate solutions and learn from examples

Understand and utilize regression

Evaluate solutions and learn from examples

Understand and utilize regression algorithms.

Understand and utilize classification algorithms

Understand and utilize clustering algorithms

Module 7: Visualizing Analytical Models with Power BI

In this module, we will explore the visualizations and options available using Power BI.

Lessons

What is Power BI

Creating a Power BI Account

Deploying to Power BI

Visualizations

Lab: Visualizing Analytical Models with Power BI

Individual Exercise: Join Datasets

Individual Exercise: Power BI

After completing this module, students will be able to:

Understand utilize joined datasets

Understand and utilize Power BI.