AI-900 Training: Microsoft Azure AI Fundamentals

Azure AI Microsoft Fundamentals AI-900

You Will Learn How 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

AI-900 Azure AI Microsoft Fundamentals

The new AI-900 course introduces important  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. The course is designed as a blended learning experience that combines instructor-led training with online materials on the Microsoft Learn platform. The hands-on exercises in the course are based on Learn modules, and students are encouraged to use the content on Learn as reference materials to reinforce what they learn in the class and to explore topics in more depth.

Is this the Right AI-900 Azure AI Microsoft Fundamentals Course for You?

The Azure AI Fundamentals course is designed for anyone interested in learning about the types of solution artificial intelligence (AI) makes possible, and the services on Microsoft Azure that you can use to create them. You don’t need to have any experience of using Microsoft Azure before taking this course, but a basic level of familiarity with computer technology and the Internet is assumed. Some of the concepts covered in the course require a basic understanding of mathematics, such as the ability to interpret charts. The course includes hands-on activities that involve working with data and running code, so a knowledge of fundamental programming principles will be helpful.

Good to know before you attend the class:

Prerequisite certification is not required before taking this course. Successful Azure AI Fundamental students start with some basic awareness of computing and internet concepts, and an interest in using Azure AI services.

Specifically:

• Experience using computers and the internet.
• Interest in use cases for AI applications and machine learning models.
• A willingness to learn through hands-on exploration.

Course Outline

AI-900T00, including lab sessions.

This outline provides a structured approach to learning AI concepts and practical skills.

Module 1: Introduction to AI

• Lecture:
  • Definition of AI
  • Types of AI (Narrow AI, General AI)
  • Applications of AI in various industries
• Lab:
  • Setting up a basic AI environment
  • Running simple AI models (e.g., using Azure Machine Learning)

Module 2: Machine Learning Basics

• Lecture:
  • Introduction to Machine Learning
  • Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
  • Key concepts (Features, Labels, Training, Testing)
• Lab:
  • Building a simple supervised learning model (e.g., linear regression)
  • Evaluating model performance

Module 3: Computer Vision

• Lecture:
  • Overview of Computer Vision
  • Image recognition and object detection
  • Applications of Computer Vision
• Lab:
  • Implementing image classification using pre-trained models
  • Using Azure Cognitive Services for computer vision tasks

Module 4: Natural Language Processing (NLP)

• Lecture:
  • Basics of NLP
  • Common NLP tasks (Text classification, Sentiment analysis, Named Entity Recognition)
  • Applications of NLP
• Lab:
  • Performing text classification using NLP libraries
  • Utilizing Azure Cognitive Services for text analytics

Module 5: Conversational AI

• Lecture:
  • Introduction to Conversational AI and chatbots
  • Key components of a chatbot (Intent, Entity, Dialogue)
  • Designing effective conversational interfaces
• Lab:
  • Building a simple chatbot using Azure Bot Service
  • Integrating the chatbot with various channels (e.g., web, social media)

Module 6: AI in the Cloud

• Lecture:
  • Benefits of using cloud services for AI
  • Overview of Azure AI services
  • Best practices for deploying AI solutions in the cloud
• Lab:
  • Deploying a machine learning model to Azure
  • Scaling AI solutions with Azure services

Module 7: Responsible AI

• Lecture:
  • Ethics in AI
  • Fairness, accountability, and transparency
  • Guidelines for building responsible AI systems
• Lab:
  • Analyzing AI models for bias
  • Implementing fairness techniques in AI development

Module 8: AI and IoT

• Lecture:
  • Integration of AI and IoT
  • Use cases and applications
  • Challenges and opportunities
• Lab:
  • Connecting IoT devices to AI models
  • Real-time data processing and analytics

Module 9: AI Project

• Lecture:
  • Planning and managing AI projects
  • Selecting the right AI techniques for the problem
  • Case studies of successful AI projects
• Lab:
  • Students work on a comprehensive AI project, applying the skills learned throughout the course
  • Project presentations and peer reviews

Final Review and Exam

• Lecture:
  • Review of key concepts and techniques
  • Preparation tips for the AI-900 certification exam
• Lab:
  • Practice exams and hands-on exercises
  • Q&A session for exam preparation

This course outline provides a comprehensive learning path from basic AI concepts to practical implementation, ensuring students are well-prepared for both the AI-900 certification exam and real-world AI applications.

Related Certifications:
Microsoft Certified: Azure AI Fundamentals
Learning Paths
Credly & Job Opportunities