home > training > ML100:Understanding Machine Learning in the Enterprise

ML100:Understanding Machine Learning in the Enterprise

$595 Interface Gold™

  • 1 Day
Dates Available
Class Time
Guaranteed to Run
Live Online
Live In Classroom
Aug 31
Replay™ AvailableThis class date includes Interface Replay™ class recordings, available for online viewing 1 hour after each class day ends.
8:30AM - 4:30PM (PHX)
This class date is Guaranteed to Run and will not change.
Guaranteed To Run
Live Online
Register today - no risk!  No cancellation fees.  Full money back guarantee!
  • This field is for validation purposes and should be left unchanged.
Course Description

Machine Learning is being implemented in many ways to solve a variety of different problems.  In this course, we will learn what kind of questions machine learning can solve, the underlying classifications employed in solutions, the information necessary to create a solution, understanding the results, tools used to create machine learning, including low and no code methods in Azure and Power BI.  Attendees will leave the course understanding what machine learning can do, what the development steps are, and how to use tools to generate a solution in your environment.


Introduction to Machine Learning and Data Science

  • Describing Machine Learning, Data Science and AI
  • The 5 different kinds of problems Machine Learning can solve
  • Technology capabilities vs hype and mirrors
  • Machine Learning classifications

Data Requirements for Machine Learning

  • All about data selection and quality
  • Understanding overfitting
  • Avoiding bias in data selection
  • Transparency solution development

The unagile Machine Learning Solution Process

  • CRISP Method
  • Understanding the data cleansing process
  • Training sets and what they are for
  • How the computer learns a from data
  • Walk through a machine learning solution step-by-step

Interpreting Results

  • Correlation and Causation
  • Different result visualizations and their meaning
  • Understanding Confusion Matrix

Implementation Options

  • Implementation Options
  • Understanding Data drift
  • Ongoing Monitoring of results
  • Planned obsolesce of the solution

Development Tools

  • A review of common languages and libraries for creating machine learning
  • No code option of development
  • Incorporating Cognitive Services
  • Machine Learning insights in Power BI