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

Code: 55162
Course duration: 2 days
Location Period
Greenwich, Connecticut (Virtual Instructor-Led)
10-12 09:00AM to 10-13 05:00PM
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10-12 09:00AM to 10-13 05:00PM
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10-12 09:00AM to 10-13 05:00PM
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10-12 09:00AM to 10-13 05:00PM
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Northern New Jersey (Virtual Instructor-Led)
10-12 09:00AM to 10-13 05:00PM
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Wilkes-Barre, Pennslyvania (Instructor-Led)
10-12 09:00AM to 10-13 05:00PM
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Secaucus, New Jersey (Virtual Instructor-Led)
10-12 09:00AM to 10-13 05:00PM
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Palm Beach County Florida (Instructor-Led)
10-12 09:00AM to 10-13 05:00PM
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Greenwich, Connecticut (Virtual Instructor-Led)
11-09 09:00AM to 11-10 05:00PM
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Fort Lauderdale, Florida (Virtual Instructor-Led)
11-09 09:00AM to 11-10 05:00PM
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Mid-Town New York City (Virtual Instructor-Led)
11-09 09:00AM to 11-10 05:00PM
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Newark, New Jersey (Virtual Instructor-Led)
11-09 09:00AM to 11-10 05:00PM
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Attend Online
11-09 09:00AM to 11-10 05:00PM
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NYC-23rd Street (Virtual Instructor-Led)
11-09 09:00AM to 11-10 05:00PM
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Northern New Jersey (Virtual Instructor-Led)
11-09 09:00AM to 11-10 05:00PM
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Wilkes-Barre, Pennslyvania (Virtual Instructor-Led)
11-09 09:00AM to 11-10 05:00PM
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Secaucus, New Jersey (Instructor-Led)
11-09 09:00AM to 11-10 05:00PM
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Palm Beach County Florida (Instructor-Led)
11-09 09:00AM to 11-10 05:00PM
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Fort Lauderdale, Florida (Virtual Instructor-Led)
12-14 09:00AM to 12-15 05:00PM
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Mid-Town New York City (Virtual Instructor-Led)
12-14 09:00AM to 12-15 05:00PM
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Newark, New Jersey (Virtual Instructor-Led)
12-14 09:00AM to 12-15 05:00PM
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Attend Online
12-14 09:00AM to 12-15 05:00PM
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NYC-23rd Street (Virtual Instructor-Led)
12-14 09:00AM to 12-15 05:00PM
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Northern New Jersey (Virtual Instructor-Led)
12-14 09:00AM to 12-15 05:00PM
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Wilkes-Barre, Pennslyvania (Virtual Instructor-Led)
12-14 09:00AM to 12-15 05:00PM
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Secaucus, New Jersey (Virtual Instructor-Led)
12-14 09:00AM to 12-15 05:00PM
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Palm Beach County Florida (Instructor-Led)
12-14 09:00AM to 12-15 05:00PM
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Greenwich, Connecticut (Instructor-Led)
12-14 09:00AM to 12-15 05:00PM
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Mid-Town New York City (Virtual Instructor-Led)
01-11 09:00AM to 01-12 05:00PM
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Newark, New Jersey (Virtual Instructor-Led)
01-11 09:00AM to 01-12 05:00PM
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Attend Online
01-11 09:00AM to 01-12 05:00PM
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NYC-23rd Street (Virtual Instructor-Led)
01-11 09:00AM to 01-12 05:00PM
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Northern New Jersey (Virtual Instructor-Led)
01-11 09:00AM to 01-12 05:00PM
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Wilkes-Barre, Pennslyvania (Virtual Instructor-Led)
01-11 09:00AM to 01-12 05:00PM
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Secaucus, New Jersey (Virtual Instructor-Led)
01-11 09:00AM to 01-12 05:00PM
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Palm Beach County Florida (Instructor-Led)
01-11 09:00AM to 01-12 05:00PM
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Greenwich, Connecticut (Virtual Instructor-Led)
01-11 09:00AM to 01-12 05:00PM
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Fort Lauderdale, Florida (Instructor-Led)
01-11 09:00AM to 01-12 05:00PM
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Mid-Town New York City (Instructor-Led)
02-15 09:00AM to 02-16 05:00PM
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Newark, New Jersey (Virtual Instructor-Led)
02-15 09:00AM to 02-16 05:00PM
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Attend Online
02-15 09:00AM to 02-16 05:00PM
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NYC-23rd Street (Virtual Instructor-Led)
02-15 09:00AM to 02-16 05:00PM
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Northern New Jersey (Virtual Instructor-Led)
02-15 09:00AM to 02-16 05:00PM
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Wilkes-Barre, Pennslyvania (Virtual Instructor-Led)
02-15 09:00AM to 02-16 05:00PM
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Secaucus, New Jersey (Virtual Instructor-Led)
02-15 09:00AM to 02-16 05:00PM
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Palm Beach County Florida (Instructor-Led)
02-15 09:00AM to 02-16 05:00PM
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Greenwich, Connecticut (Virtual Instructor-Led)
02-15 09:00AM to 02-16 05:00PM
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Fort Lauderdale, Florida (Virtual Instructor-Led)
02-15 09:00AM to 02-16 05:00PM
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Greenwich, Connecticut (Virtual Instructor-Led)
03-15 09:00AM to 03-16 05:00PM
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Fort Lauderdale, Florida (Virtual Instructor-Led)
03-15 09:00AM to 03-16 05:00PM
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Mid-Town New York City (Virtual Instructor-Led)
03-15 09:00AM to 03-16 05:00PM
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Newark, New Jersey (Virtual Instructor-Led)
03-15 09:00AM to 03-16 05:00PM
enroll
Attend Online
03-15 09:00AM to 03-16 05:00PM
enroll
NYC-23rd Street (Instructor-Led)
03-15 09:00AM to 03-16 05:00PM
enroll
Northern New Jersey (Virtual Instructor-Led)
03-15 09:00AM to 03-16 05:00PM
enroll
Wilkes-Barre, Pennslyvania (Virtual Instructor-Led)
03-15 09:00AM to 03-16 05:00PM
enroll
Secaucus, New Jersey (Virtual Instructor-Led)
03-15 09:00AM to 03-16 05:00PM
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Palm Beach County Florida (Instructor-Led)
03-15 09:00AM to 03-16 05:00PM
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55162 - Creating and Deploying in Minutes No-Code Predictive Analytics Using Azure Machine Learning (2 Days)

About This Course

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.

At Course Completion

  • 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.

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

Lab 1: 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 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
  • 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 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 1: 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.

Prerequisites:

  • Working knowledge of their own business data.

Guaranteed to Run

2018-03-20 09:00 to 2018-03-23 17:00
Palm Beach County Florida (Instructor-Led)
2018-03-19 09:00 to 2018-03-23 17:00
Secaucus, New Jersey (Virtual Instructor-Led)
2018-03-01 09:00 to 2018-03-02 17:00
Palm Beach County Florida (Instructor-Led)

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