Course

Course Summary
Credit Type:
Course
ACE ID:
MLS-0101
Organization's ID:
APL-3004
Organization:
Microsoft Corporation
Location:
Online
Length:
5-8 hours
Dates Offered:
Credit Recommendation & Competencies
Competency Framework Statement
AI Competency Framework Understanding Data: 1.1 Employ different types of data and their representations
AI Competency Framework Understanding Data: 1.2 Analyze typical uses of data in machine learning (ML) and AI
AI Competency Framework Data Handling and Manipulation: 3.1 Prepare data for use in an ML or AI project
AI Competency Framework Data Handling and Manipulation: 3.2 Manipulate data
AI Competency Framework Core Language Skills: 1.1 Write code using proper syntax and structure
AI Competency Framework Core Language Skills: 1.2 Incorporate libraries
AI Competency Framework Core Language Skills: 1.3 Improve code performance
AI Competency Framework Data Reprocessing: 1.1 Prepare features for use in supervised or non-supervised learning tasks
AI Competency Framework Supervised Learning: 2.1 Manage a supervised learning framework
AI Competency Framework Supervised Learning: 2.2 Apply supervised learning to specific tasks
AI Competency Framework Unsupervised Learning: 3.1 Manage an unsupervised learning framework
AI Competency Framework Artificial Neural Networks: 2.1 Use general multi-layer neural networks
AI Competency Framework Artificial Neural Networks: 2.2 Use specific deep learning models
AI Competency Framework Data Storage: 1.1 Manipulate data stored in files
AI Competency Framework Data Storage: 2.1 Manipulate data stored in databases
AI Competency Framework Cloud Computing: 3.1 Use different types of cloud architectures
AI Competency Framework Tools: 3. Configure the tool
AI Competency Framework Tools: 4. Find documentation for the tool
Building Blocks Foundational Tier 1 this is a test 08.25.2022
AI Competency Framework AI Fundamentals: 1.1. Apply technical concepts based on hybrid AI knowledge
AI Competency Framework Prototyping and Testing: 4.1 Create a prototype that integrates AI components
Description

Objective:

The course objective is to demonstrate the ability to build computer vision solutions by using Azure AI Vision.

Candidates for this credential should have a solid understanding of working with Azure AI Vision models, both prebuilt and custom, through Vision Studio and in code. They should also have experience programming in either Python or C#, be familiar with the Azure portal, and be comfortable provisioning Azure AI resources.

Learning Outcomes:

  • Create a computer vision resource
  • Analyze images
  • Create a custom image analysis model
  • Train and evaluate the model
  • Consume a custom model

General Topics:

  • Create a computer vision resource
  • Analyze images
  • Create a custom image analysis model
  • Train and evaluate the model
  • Consume a custom model
Instruction & Assessment

Instructional Strategies:

  • Computer Based Training
  • Practical Exercises

Methods of Assessment:

  • Other
  • Performance based test

Minimum Passing Score:

70%
Supplemental Materials