Ai-102: Microsoft Certified Azure Ai Engineer Associate

Prepare for the AI-102 - Microsoft Certified Azure AI Engineer Associate exam

What you'll learn

Understand the core concepts of AI, machine learning, and generative AI in the context of Azure.

Plan, build, deploy, and manage AI solutions using Azure AI Foundry services.

Select and implement the appropriate Azure service for solutions involving: Generative AI, Computer Vision, Natural language processing.

Develop Python programs that interact with Azure AI services and APIs.

Fine-tune models, apply prompt engineering, and implement RAG (Retrieval-Augmented Generation) solutions.

Requirements

Basic familiarity with cloud computing concepts (recommended but not mandatory).

Fundamental understanding of programming, ideally with some experience in Python.

Access to an Azure account to perform hands-on labs

Interest in learning how to build real-world AI solutions using Microsoft Azure.

No prior experience with Azure AI services is required — concepts will be taught from scratch

Description

Unlock your potential and step confidently into the world of AI with our AI-102: Azure AI Engineer Associate Exam Preparation Course! Whether you’re aiming for the prestigious Microsoft certification or looking to elevate your career by mastering Azure AI, this course gives you the edge you need.This hands-on, beginner-friendly program goes beyond just theory — you’ll learn how to design, build, and deploy real-world AI solutions using Microsoft Azure. From generative AI and computer vision to natural language processing, speech, and knowledge mining, you’ll discover how to harness the full power of Azure AI services to create intelligent, impactful applications.With clear explanations, practical Python coding examples, and guided labs, you’ll gain the confidence to solve real-world challenges while also preparing for exam success. You’ll also learn how to implement responsible AI practices, apply prompt engineering, fine-tune models, and integrate solutions into production environments seamlessly.If you’re a developer, data professional, or an aspiring AI engineer looking to future-proof your skills and showcase your expertise, this course is for you. By the end, you’ll be ready to pass the AI-102 exam — and even more importantly, to build solutions that make a difference.Start your journey today — your future in AI starts here!

Overview

Section 1: Planning an Azure AI solution

Lecture 1 Introduction

Lecture 2 A brief understanding on Machine Learning

Lecture 3 Generative AI

Lecture 4 Let’s start with ChatGPT

Lecture 5 What are Large Language Models

Lecture 6 What is GPT(Generative Pre-Trained Transformer)

Lecture 7 OpenAI - Understanding the Model Lineup

Lecture 8 What is a Multi-modal LLM

Lecture 9 OpenAI API Platform - Sign-up

Lecture 10 OpenAI API Platform- Using the playground

Lecture 11 OpenAI API Platform - System Messages

Lecture 12 What’s next

Lecture 13 Introduction to Azure

Lecture 14 The Azure Free Account

Lecture 15 Creating the Azure Free Account

Lecture 16 Tour around the Azure portal

Lecture 17 Azure AI services

Lecture 18 AI Safety and Responsibility

Section 2: Implement Generative AI solutions

Lecture 19 Azure Open AI and Azure AI Foundry

Lecture 20 Deploying an Azure AI Foundry resource - Overview

Lecture 21 Lab - Creating an Azure AI Foundry resource

Lecture 22 Launching the Azure AI Foundry portal

Lecture 23 Azure AI Foundry - Exploring the models

Lecture 24 Azure AI Foundry - Deploying a model - Overview

Lecture 25 Lab - Azure AI Foundry - Deploying a model

Lecture 26 Lab - Azure AI Foundry - Using the playground

Lecture 27 Lab - Azure AI Foundry - Model parameters

Lecture 28 Lab - Azure AI Foundry - System prompt

Lecture 29 Lab - Azure AI Foundry - Image Generation

Lecture 30 Prompt Engineering - Basic Prompt fundamentals

Lecture 31 Prompt Engineering - Zero-shot Prompting

Lecture 32 Prompt Engineering - Few-shot Prompting

Lecture 33 Prompt Engineering - Chain-of-Thought Prompting

Lecture 34 Lab - Installing Python

Lecture 35 Lab - Installing Visual Studio Code

Lecture 36 Lab - Running a simple Python program

Lecture 37 Lab - Python - GPT 4.1 - Initial step

Lecture 38 Lab - Python - GPT 4.1 - Chat Completion API

Lecture 39 Lab - Python - GPT 4.1 - System Messages

Lecture 40 Lab - Python - GPT 4.1 - Getting the entire response

Lecture 41 Quick note on monitoring the model usage

Lecture 42 Lab - Multi-Modal support for models

Lecture 43 Lab - Python - GPT 4.1 - Sending an image as input

Lecture 44 Lab - Python - GPT 4.1 - Asking the model to explain code

Lecture 45 Lab - Python - Using a model to generate an image

Lecture 46 Fine Tune Azure OpenAI models

Lecture 47 Overview of Fine tuning a model

Lecture 48 Retrieval Augmented Generation

Lecture 49 Lab - Retrieval Augmented Generation - Azure Blob service

Lecture 50 Lab - Retrieval Augmented Generation - Azure Search service

Lecture 51 Lab - Retrieval Augmented Generation - Implementation

Lecture 52 Lab - Retrieval Augmented Generation - Using Python

Lecture 53 Azure AI Content Safety

Lecture 54 Lab - Azure AI Content Safety - Adding a content filer

Lecture 55 Azure AI Content Safety - BlockList - Quick note

Lecture 56 Lab - Creating an Azure AI Content Safety resource

Lecture 57 Lab - Azure Safety Content Studio

Lecture 58 Lab - Azure AI Content Safety Studio - Prompt Shield

Lecture 59 Lab - Python - Azure AI Content Safety as a service - Detect Harmful images

Lecture 60 Lab - Python - Azure AI Content Safety as a service - Detect Harmful text

Lecture 61 Lab - Azure AI Content Safety - Making REST API calls

Section 3: Implement computer vision solutions

Lecture 62 Introduction to Computer Vision

Lecture 63 Lab - Creating a computer vision resource

Lecture 64 Lab - Computer Vision - Image Tagging

Lecture 65 Lab - Computer Vision - Python - Image Tagging

Lecture 66 Lab - Computer Vision - Python - Generate caption

Lecture 67 Lab - Computer Vision - Python - Object Detection

Lecture 68 Lab - Computer Vision - Python - Optical Character Recognition

Lecture 69 Lab - Computer Vision - Python - Brand Detection

Lecture 70 The Face service

Lecture 71 Lab - Face service

Lecture 72 Lab - Python - Face service

Lecture 73 Azure AI Custom Vision

Lecture 74 Lab - Azure AI Custom Vision - Create the Azure resources

Lecture 75 Lab - Azure AI Custom Vision - Image classification model

Lecture 76 Quick understanding on the training results

Lecture 77 Lab - Azure AI Custom Visionb - Python - Image classification

Lecture 78 Azure AI Custom Vision - Object detection model

Lecture 79 Azure AI Video Indexer

Lecture 80 Lab - Azure AI Video Indexer

Section 4: Implement natural language processing solutions

Lecture 81 Azure AI Language service

Lecture 82 Lab - Azure AI Language - Detect Language

Lecture 83 Lab - Azure AI Language resource

Lecture 84 Lab - Visual Studio Code - Making API calls

Lecture 85 Lab - Azure AI Language - Detect Language - API Call

Lecture 86 Lab - Azure AI Language - Detect Language - Python

Lecture 87 Lab - Azure AI Language - Extract key phrases

Lecture 88 Lab - Azure AI Language - Extract key phrases - Python

Lecture 89 Lab - Azure AI Language - Analyze sentiments

Lecture 90 Lab - Azure AI Language - Analyze sentiments - Python

Lecture 91 Lab - Azure AI Language - Extract Entities

Lecture 92 Lab - Azure AI Language - Extract Entities - Python

Lecture 93 Lab - Azure AI Language - Summarization service

Lecture 94 Lab - Azure AI Language - Personally Identifiable Information

Lecture 95 Azure AI Translator service

Lecture 96 Azure Speech Service

Lecture 97 Lab - Azure Speech - Text to Speech

Lecture 98 Lab - Azure Speech - Text to Speech - Python

Lecture 99 Lab - Speech Synthesis Markup Language

Lecture 100 Lab - Azure Speech - Speech to text

Lecture 101 Lab - Azure Speech - Speech to text - Python - Using local file

Lecture 102 Lab - Azure Speech - Speech to text - Python - Using Microphone

Lecture 103 Lab - Azure Speech - Speech Translation service

Lecture 104 Conversational Language Understanding - Introduction

Lecture 105 Lab - Getting started with Language Studio

Lecture 106 Lab - Conversational Language Understanding - Adding Intents

Lecture 107 Lab - Conversational Language Understanding - Adding Entities

Lecture 108 Lab - Conversational Language Understanding - Adding Utterances

Lecture 109 Lab - Conversational Language Understanding - Training the Model

Lecture 110 Lab - Conversational Language Understanding - Deploying the Model

Lecture 111 Lab - Python - Consume Conversational Language Model

Lecture 112 Conversational Language Project - Backup - Note

Lecture 113 Custom question answering - Introduction

Lecture 114 Lab - Custom question answering - Getting started

Lecture 115 Lab - Custom question answering - Edit the knowledge base

Lecture 116 Lab - Custom question answering - Deploy and use the knowledge base

Lecture 117 Lab - Creating a Bot

Section 5: Practice Section

Students and professionals preparing for the AI-102: Azure AI Engineer Associate certification exam,Developers who want to learn how to build and deploy AI solutions using Azure,Data scientists, engineers, and solution architects looking to enhance their AI skills with hands-on Azure experience,Professionals interested in applying responsible AI practices in their application