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Creating and Deploying in Minutes No-Code Predictive Analytics Using Azure Machine Learning - 55162 Course Outline

 (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.

Audience profile
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.

Before attending this course, students must have:
Working knowledge of their own business data.

At course completion
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 Outline

Module 1: Course Overview
This module explains how the class will be structured and introduces course materials and additional administrative information.
Course Materials
What We'll Be Discussing
Lab : Course Overview
Creating Your Azure Machine Learning Account

Module 2: What is Machine Learning?
In this module, we will explain machine learning and the concepts behind it.
One Methodology
Supervised vs. Unsupervised Methods
Analytics Spectrum
Development Methodology with Azure Machine Learning Studio
Be Very Vigilant
Lab : What is Machine Learning?

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.
 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

Module 4: Data Preparation
In this module, we will cover the steps necessary for data cleaning and explore other data preparation techniques.
 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

Module 5: Machine Learning Algorithms
In this module, we will explain the different types of algorithms available and their uses.
Anomaly Detection
Azure Machine Learning Cheat Sheet
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

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.
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 : 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
Individual Exercise: Clustering

Module 7: Visualizing Analytical Models with Power BI
In this module, we will explore the visualizations and options available using Power BI.
What is Power BI?
Creating a Power BI Account
Deploying to Power BI
Lab : Visualizing Analytical Models with Power BI
Individual Exercise: Join Datasets
Individual Exercise: Power BI
View outline in Word


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