Description

Candidates for this exam should have the foundational knowledge of Machine Learning (ML) and Artificial Intelligence (AI) concepts and related Microsoft Azure services.

This course introduces fundamentals concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions. The course is not designed to teach students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services to support them.

Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them.

Objectives

After completing this course, students will be able to:

• Describe Artificial Intelligence workloads and considerations
• Describe fundamental principles of machine learning on Azure
• Describe features of computer vision workloads on Azure
• Describe features of Natural Language Processing (NLP) workloads on Azure
• Describe features of conversational AI workloads on Azure

Who Should Attend

• Candidates with both technical and non-technical backgrounds

Prerequisites

• Should have general programming knowledge or experience
• Should have a foundational knowledge of Machine Learning (ML) and Artificial Intelligence (AI) concepts
• Should have knowledge of Microsoft Azure services

Course Outline

Module 1: Describe Artificial Intelligence Workloads and Considerations

• Identify Features of Common AI Workloads
  • Identify Prediction / Forecasting Workloads
  • Identify Features of Anomaly Detection Workloads
  • Identify Computer Vision Workloads
  • Identify Natural Language Processing or Knowledge Mining Workloads
  • Identify Conversational AI Workloads
• Identify Guiding Principles for Responsible AI
  • Describe Considerations for Fairness in an AI Solution
  • Describe Considerations for Reliability and Safety in an AI Solution
  • Describe Considerations for Privacy and Security in an AI Solution
  • Describe Considerations for Inclusiveness in an AI Solution
  • Describe Considerations for Transparency in an AI Solution
  • Describe Considerations for Accountability in an AI Solution

Module 2: Describe Fundamental Principles of Machine Learning on Azure

• Identify common Machine Learning types
  • identify regression Machine Learning scenarios
  • Identify Classification Machine Learning scenarios
  • Identify Clustering Machine Learning scenarios
• Describe core Machine Learning concepts
  • Identify Features and Labels in a Dataset for Machine Learning
  • Describe How Training and Validation Datasets are used in Machine Learning
  • Describe How Machine Learning Algorithms are used for Model Training
  • Select and Interpret Model Evaluation Metrics for Classification and Regression
• Identify Core Tasks in Creating a Machine Learning Solution
  • Describe Common Features of Data Ingestion and Preparation
  • Describe Common Features of Feature Selection and Engineering
  • Describe Common Features of Model Training and Evaluation
  • Describe Common Features of Model Deployment and Management
• Describe Capabilities of no-code Machine Learning with Azure Machine Learning
  • Automated Machine Learning tool
  • Azure Machine Learning designer

Module 3: Describe Features of Computer Vision Workloads on Azure

• Identify Common Types of Computer Vision Solution
  • Identify Features of Image Classification Solutions
  • Identify Features of Object Detection Solutions
  • Identify Features of Semantic Segmentation Solutions
  • Identify Features of Optical Character Recognition Solutions
  • Identify Features of Facial Detection, Recognition, and Analysis Solutions
• Identify Azure tools and Services for Computer Vision Tasks
  • Identify Capabilities of the Computer Vision Service
  • Identify Capabilities of the Custom Vision Service
  • Identify Capabilities of the Face Service
  • Identify Capabilities of the Form Recognizer Service
• Describe features of Natural Language Processing (NLP) Workloads on Azure
  • Identify Features of Common NLP Workload Scenarios
  • Identify Features and Uses for Key Phrase Extraction
  • Identify Features and Uses for Entity Recognition
  • Identify Features and Uses for Sentiment Analysis
  • Identify Features and Uses for Language Modeling
  • Identify Features and Uses for Speech Recognition and Synthesis
  • Identify Features and Uses for Translation
• Identify Azure tools and Services for NLP Workloads
  • Identify Capabilities of the Text Analytics Service
  • Identify Capabilities of the Language Understanding Intelligence Service (LUIS)
  • Identify Capabilities of the Speech Service
  • Identify Capabilities of the Text Translator Service

Module 4: Describe Features of Conversational AI Workloads on Azure

• Identify Common use Cases for Conversational AI
  • Identify Features and Uses for Webchat Bots
  • Identify Features and Uses for Telephone Voice Menus
  • Identify Features and Uses for Personal Digital Assistants
• Identify Azure Services for conversational AI
  • Identify Capabilities of the QnA Maker Service
  • Identify Capabilities of the Bot Framework

About the Trainer

A Certified Microsoft Azure Trainer

Other Details

Questions?

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