IL - AI-900 Microsoft Azure AI Fundamentals
In this course you will be introduced to the fundamentals concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions. You will have an introduction to AI, learn about Computer Vision and Machine Learning along with Natural Language Processing and Conversational AI. This course will help prepare you to pass the Microsoft certification exam AI-900 Microsoft Azure AI Fundamentals.
- Duration: 1 day
- Level: 100
Who this course is designed for
- AI Engineers
- Data Scientists
- Solutions Architects
- After completing this module you will be able to describe features of conversational AI workloads on Azure
- Familiarity with computers and using a web browser.
Module 01- Identify Features of Common AI Workloads
In this module you will learn how to identify prediction/forecasting workloads, features of anomaly detection workloads and computer vision workloads. This module will also cover how to identify natural language processing or knowledge mining workloads as well as conversational AI workloads.
Module 02 - Identify Guiding Principles for Responsible AI
This module will cover considerations for fairness in an AI solution, for reliability and safety in an AI solution, for privacy and security in an AI solution and for inclusiveness in an AI solution. You will also learn about considerations for transparency in an AI solution and for accountability in an AI solution.
Module 03 - Identify Common Machine Learning Types
In this module we will cover regression machine learning scenarios, classification machine learning scenarios and clustering machine learning scenarios.
Module 04 - Describe Core Machine Learning Concepts
In this module you will learn about the features and labels in a dataset for machine learning and how training and validation datasets are used in machine learning. This module will also cover how machine learning algorithms are used for model training and how to select and interpret model evaluation metrics for classification and regression.
Module 05 - Identify Core Tasks in Creating a Machine Learning Solution
In this module you will learn about the common features of data ingestion and preparation, features of feature selection and engineering, features of model training and evaluation and common features of model deployment and management.
Module 06 - Describe Capabilities of No-Code Machine Learning with Azure Machine Learning
In this module you will learn about the automated Machine Learning tool as well as the Azure Machine Learning designer.
Module 07 - Identify Common Types of Computer Vision Solution
In this module we will cover features of image classification solutions, object detection solutions and semantic segmentation solutions. You will also learn about the features of optical character recognition solutions, facial detection, recognition, and analysis solutions.
Module 08 - Identify Azure Tools and Services for Computer Vision Tasks
In this module you will learn about the capabilities of the Computer Vision service, Custom Vision service, Face service and Form Recognizer service.
Module 09 - Identify Features of Common NLP Workload Scenarios
In this module you will learn about the features and uses for key phrase extraction, entity recognition, sentiment analysis and language modeling. We will also cover the uses for speech recognition and synthesis and translation.
Module 10 - Identify Azure Tools and Services for NLP Workloads
In this module you will learn about the capabilities of the Text Analytics service, Language Understanding Intelligence Service (LUIS), Speech service and Text Translator service.
Module 11 - Identify Common Use Cases for Conversational AI
In this module you will learn about the features and uses for webchat bots, telephone voice menus and personal digital assistants.
Module 12 - Identify Azure Services for Conversational AI
In this module you will learn about the capabilities of the QnA Maker service and the Bot Framework.