AI-900: MICROSOFT AZURE AI FUNDAMENTALS

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The AI-900: Microsoft Azure AI Fundamentals Syllabus provides foundational knowledge of artificial intelligence concepts and Microsoft Azure AI services. It covers essential topics such as machine learning, computer vision, natural language processing, conversational AI, and responsible AI principles. This training explains how AI solutions are developed and deployed using Azure’s cloud-based tools. It is important because understanding AI fundamentals is essential for working with modern intelligent applications and cloud technologies. It is ideal for beginners, students, business professionals, developers, and IT enthusiasts who want to start their journey in artificial intelligence and Azure AI services.

Duration of Training : 20-24 Hours
Batch type : Weekdays/Weekends
Mode of Training : Classroom/Online/Corporate Training
Detailed Syllabus • Hands-on Labs • Assignments • Support-Focused • Implementation

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Overview

The Syllabus

Curriculum Designed by Experts

🎯 Program Objective

This program is designed to: Build strong foundational understanding of AI concepts
Provide hands-on exposure to Azure AI services
Enable learners to identify AI use cases in business
Prepare candidates for AI-900 certification
👉 Suitable for:
Freshers
Business Analysts
Non-programmers entering AI
Cloud beginners

MODULE 1: Introduction to Artificial Intelligence (3–4 Hours)

🔹 Core Topics
Definition and evolution of AI
Types of AI:
Narrow AI vs General AI
AI vs Machine Learning vs Deep Learning
🔹 AI Workloads Explained
Machine Learning
Computer Vision
Natural Language Processing (NLP)
Conversational AI
Generative AI
🔹 Real-World Use Cases
Banking: Fraud detection
Retail: Recommendation engines
Healthcare: Diagnosis systems
IT: Chatbots
🔹 Key Concepts
Data-driven decision making
Pattern recognition
Automation using AI
🧪 Assignment
👉 Identify 5 AI use cases in your current domain
💼 Scenario
👉 A company wants to reduce manual support effort using AI chatbot

MODULE 2: Project Planning Basics (4 Hours)

🔹 Topics
Overview of Microsoft Azure ecosystem
What is Microsoft Azure
Azure AI services architecture
🔹 Azure AI Services Overview
Azure AI Vision
Azure AI Language
Azure AI Speech
Azure OpenAI
🔹 Concepts
Cloud-based AI vs on-premise AI
API-based AI consumption
🔹 Hands-On
Azure Portal navigation
Creating AI resources
🧪 Assignment
👉 Create Azure account and explore AI services
💼 Scenario
👉 Company wants to use prebuilt AI APIs instead of building models

MODULE 3: Machine Learning Fundamentals (4–5 Hours)

🔹 Topics
What is Machine Learning?
Types:
Supervised Learning (Regression, Classification)
Unsupervised Learning (Clustering)
Reinforcement Learning
🔹 Core Concepts
Features & labels
Training vs testing data
Overfitting vs underfitting
Model evaluation metrics
🔹 Azure ML Concepts
Azure Machine Learning workspace
Low-code ML tools
🧪 Assignment
👉 Identify ML use case (sales prediction / fraud detection)
💼 Scenario
👉 Predict customer churn using historical data

MODULE 4: Computer Vision (3–4 Hours)

🔹 Topics
Image classification
Object detection
Face recognition
Optical Character Recognition (OCR)
🔹 Tools
Azure AI Vision
🔹 Use Cases
Invoice scanning
Security surveillance
Medical imaging
🧪 Hands-On
👉 Upload images and analyze results
💼 Scenario
👉 Automate document processing using OCR

MODULE 5: Natural Language Processing (NLP) (3–4 Hours)

🔹 Topics
Text analytics
Sentiment analysis
Named entity recognition
Language detection
🔹 Concepts
Tokenization
Language models
🔹 Tools
Azure AI Language
🧪 Assignment
👉 Perform sentiment analysis on reviews
💼 Scenario
👉 Analyze customer feedback from social media

MODULE 6: Speech AI (2–3 Hours)

🔹 Topics
Speech-to-text
Text-to-speech
Speech translation
🔹 Tools
Azure AI Speech
🔹 Use Cases
Voice assistants
Call center automation
🧪 Assignment
👉 Convert voice input into text
💼 Scenario
👉 Build voice-based support system

MODULE 7: Generative AI & Azure OpenAI (2–3 Hours)

🔹 Topics
What is Generative AI
Large Language Models (LLMs)
Prompt engineering basics
🔹 Tools
Azure OpenAI
🔹 Concepts
Prompt design
Responsible usage
🧪 Assignment
👉 Create prompts for chatbot responses
💼 Scenario
👉 Build AI assistant for FAQs

MODULE 8: Responsible AI (2–3 Hours)

🔹 Topics
Fairness
Reliability & safety
Privacy & security
Transparency & explainability
🔹 Concepts
Bias in AI
Ethical AI practices
🧪 Assignment
👉 Identify risks in AI system
💼 Scenario
👉 AI hiring system showing bias

🧪 CAPSTONE PROJECTS (IMPORTANT)

🔥 Project 1: AI Chatbot
Use Azure AI / OpenAI
Build simple chatbot
Handle business queries
🔥 Project 2: Image Processing System
Upload images
Detect objects/text
Generate structured output
🔥 Project 3: Customer Feedback Analyzer
Analyze reviews
Sentiment classification
Business insights

💼 REAL-TIME JOB SCENARIOS

🔹 Business Scenarios
Automating customer support
Predicting sales trends
🔹 Technical Scenarios
Poor model accuracy
Data quality issues
🔹 Ethical Scenarios
AI bias
Privacy violations

🎯 Skills You Gain

AI fundamentals understanding

Azure AI services usage

Business AI use case mapping

Basic prompt engineering

🎯 Roles After Training

AI Analyst

Business Analyst with AI

Cloud AI Associate

Pre-Sales Consultant

💰 Salary Insight (India)

Fresher: ₹4–8 LPA

With AI + Cloud: ₹8–15 LPA

📜 Certification

👉 AI-900 Certification by:
Microsoft

🚀 Final Outcome

After completion, learners will:
✔ Understand AI concepts clearly
✔ Use Azure AI services confidently
✔ Identify AI opportunities in business
✔ Be ready for AI-900 certification

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AI-900: Microsoft Azure AI Fundamentals Course Certification With Training In Bangalore

Q: 🎯 Program Objective

This program is designed to: Build strong foundational understanding of AI concepts Provide hands-on exposure to Azure AI services Enable learners to identify AI use cases in business Prepare candidates for AI-900 certification 👉 Suitable for: Freshers Business Analysts Non-programmers entering AI Cloud beginners

Q: MODULE 1: Introduction to Artificial Intelligence (3–4 Hours)

🔹 Core Topics Definition and evolution of AI Types of AI: Narrow AI vs General AI AI vs Machine Learning vs Deep Learning 🔹 AI Workloads Explained Machine Learning Computer Vision Natural Language Processing (NLP) Conversational AI Generative AI 🔹 Real-World Use Cases Banking: Fraud detection Retail: Recommendation engines Healthcare: Diagnosis systems IT: Chatbots 🔹 Key Concepts Data-driven decision making Pattern recognition Automation using AI 🧪 Assignment 👉 Identify 5 AI use cases in your current domain 💼 Scenario 👉 A company wants to reduce manual support effort using AI chatbot

Q: MODULE 2: Project Planning Basics (4 Hours)

🔹 Topics Overview of Microsoft Azure ecosystem What is Microsoft Azure Azure AI services architecture 🔹 Azure AI Services Overview Azure AI Vision Azure AI Language Azure AI Speech Azure OpenAI 🔹 Concepts Cloud-based AI vs on-premise AI API-based AI consumption 🔹 Hands-On Azure Portal navigation Creating AI resources 🧪 Assignment 👉 Create Azure account and explore AI services 💼 Scenario 👉 Company wants to use prebuilt AI APIs instead of building models

Q: MODULE 3: Machine Learning Fundamentals (4–5 Hours)

🔹 Topics What is Machine Learning? Types: Supervised Learning (Regression, Classification) Unsupervised Learning (Clustering) Reinforcement Learning 🔹 Core Concepts Features & labels Training vs testing data Overfitting vs underfitting Model evaluation metrics 🔹 Azure ML Concepts Azure Machine Learning workspace Low-code ML tools 🧪 Assignment 👉 Identify ML use case (sales prediction / fraud detection) 💼 Scenario 👉 Predict customer churn using historical data

Q: MODULE 4: Computer Vision (3–4 Hours)

🔹 Topics Image classification Object detection Face recognition Optical Character Recognition (OCR) 🔹 Tools Azure AI Vision 🔹 Use Cases Invoice scanning Security surveillance Medical imaging 🧪 Hands-On 👉 Upload images and analyze results 💼 Scenario 👉 Automate document processing using OCR

Q: MODULE 5: Natural Language Processing (NLP) (3–4 Hours)

🔹 Topics Text analytics Sentiment analysis Named entity recognition Language detection 🔹 Concepts Tokenization Language models 🔹 Tools Azure AI Language 🧪 Assignment 👉 Perform sentiment analysis on reviews 💼 Scenario 👉 Analyze customer feedback from social media

Q: MODULE 6: Speech AI (2–3 Hours)

🔹 Topics Speech-to-text Text-to-speech Speech translation 🔹 Tools Azure AI Speech 🔹 Use Cases Voice assistants Call center automation 🧪 Assignment 👉 Convert voice input into text 💼 Scenario 👉 Build voice-based support system

Q: MODULE 7: Generative AI & Azure OpenAI (2–3 Hours)

🔹 Topics What is Generative AI Large Language Models (LLMs) Prompt engineering basics 🔹 Tools Azure OpenAI 🔹 Concepts Prompt design Responsible usage 🧪 Assignment 👉 Create prompts for chatbot responses 💼 Scenario 👉 Build AI assistant for FAQs

Q: MODULE 8: Responsible AI (2–3 Hours)

🔹 Topics Fairness Reliability & safety Privacy & security Transparency & explainability 🔹 Concepts Bias in AI Ethical AI practices 🧪 Assignment 👉 Identify risks in AI system 💼 Scenario 👉 AI hiring system showing bias

Q: 🧪 CAPSTONE PROJECTS (IMPORTANT)

🔥 Project 1: AI Chatbot Use Azure AI / OpenAI Build simple chatbot Handle business queries 🔥 Project 2: Image Processing System Upload images Detect objects/text Generate structured output 🔥 Project 3: Customer Feedback Analyzer Analyze reviews Sentiment classification Business insights

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