AI-900: MICROSOFT AZURE AI FUNDAMENTALS + AI-102: DESIGNING AND IMPLEMENTING A MICROSOFT AZURE AI SOLUTION

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AI-900: Microsoft Azure AI Fundamentals Training introduces the basics of Artificial Intelligence & Machine Learning concepts with Microsoft Azure services. It is important as it helps learners understand AI use cases, Azure AI tools & responsible AI practices. This training is ideal for beginners, business users, students & non-technical professionals looking to build foundational AI knowledge without prior programming or ML experience.

Duration of Training : 44 Hours
Batch type : Weekdays/Weekends
Mode of Training : Classroom/Online/Corporate Training

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Overview

The Syllabus

Curriculum Designed by Experts

AI-900: Microsoft Azure AI Fundamentals – Training Syllabus

Duration: 12 Hours
Target Audience: Beginners, Business Users, Students, Non-technical professionals wanting to understand AI on Azure.
Prerequisites: No prior AI/ML or programming experience required.

Module 1: Introduction to Artificial Intelligence

• What is Artificial Intelligence?
• AI Workloads & Applications
• Machine Learning vs Deep Learning vs AI
• Responsible AI Principles (Fairness, Transparency, Privacy, Accountability)
Assignment: Identify AI applications in Healthcare, Retail, and Finance.

Module 2: Fundamentals of Machine Learning

• What is Machine Learning?
• Supervised, Unsupervised, Reinforcement Learning
• Features, Labels, and Training Data
• Overfitting & Underfitting Concepts
• ML Lifecycle Basics
Hands-On Lab: Use Azure Machine Learning Studio to build a simple classification model.

Module 3: Azure Machine Learning Services

• Introduction to Azure Machine Learning (Azure ML)
• AutoML in Azure
• No-code ML model building in Azure ML Studio
• Model deployment concepts
Assignment: Build a prediction model for loan approvals using AutoML

Module 4: Computer Vision on Azure

• Basics of Image Classification & Object Detection
• Azure Computer Vision Service
• Azure Custom Vision Service (Training & Prediction)
• OCR (Optical Character Recognition) in Azure
• Face Detection and Analysis
Hands-On Lab: Create a Custom Vision model to classify product images.

Module 5: Natural Language Processing (NLP) on Azure

• Language Understanding Concepts (LUIS)
• Text Analytics (Sentiment Analysis, Key Phrase Extraction)
• Speech Recognition and Speech Synthesis
• Translator Service in Azure
Hands-On Lab: Use Azure Cognitive Services to perform sentiment analysis on customer feedback.

Module 6: Conversational AI on Azure

• Basics of Conversational AI & Chatbots
• Azure Bot Service & QnA Maker
• Integrating Chatbots with Teams/Website
• Building FAQ Bots with Cognitive Services
Hands-On Lab: Build a QnA chatbot for a company knowledge base.

Module 7: Responsible AI and Security

• Bias and Fairness in AI
• AI Security and Data Privacy
• Governance and Compliance in Azure AI
• Tools for Monitoring AI Models
Assignment: Discuss an ethical AI case study where bias was detected in a model.

Final Capstone Project

Build an AI-powered Customer Support System:
1. Use Azure Computer Vision to extract text from images (customer complaints).
2. Perform sentiment analysis on text using Text Analytics.
3. Build a QnA chatbot using Azure Bot Service to handle FAQs.

🎓 Certification Exam (AI-900) Overview

• Exam Duration: 45–60 minutes
• Question Format: Multiple-choice, Case studies, Drag & Drop
• Passing Score: 700/1000
• Skills Measured:
o Describe AI workloads & Responsible AI
o Describe fundamentals of ML on Azure
o Describe features of Computer Vision on Azure
o Describe features of NLP on Azure
o Describe features of Conversational AI on Azure

Deliverables from Training:

• 10+ Hands-on Labs
• 20+ Assignments
• 1 Capstone Project
• Practice Exam Questions

AI-102: Designing and Implementing a Microsoft Azure AI Solution

Duration: 32 Hours (5–6 Weeks)
Level: Associate Level – Good for Beginners to 5+ year experienced professional
Prerequisites:
• Knowledge of AI/ML concepts (AI-900 recommended)
• Familiarity with Python or C#
• Basic Azure experience (subscriptions, resource groups, Azure portal)

Module 1: Introduction to AI on Azure

• AI Solution Development Lifecycle
• Azure Cognitive Services Overview
• Provisioning Cognitive Services in Azure
• Authentication & Security (Keys, Endpoints, RBAC, Managed Identity)
Assignment: Create a resource group and deploy a Cognitive Services resource.

Module 2: Computer Vision Solutions

• Azure Computer Vision API (Image Analysis, OCR, Read API)
• Face API (Face Detection, Recognition, Verification)
• Custom Vision (Image Classification, Object Detection, Model Training)
• Video Indexer for video insights
Hands-On Lab:
• Build an image classification model using Azure Custom Vision
• Perform OCR on scanned documents

Module 3: Natural Language Processing (NLP)

• Text Analytics (Key Phrases, Sentiment Analysis, Named Entity Recognition)
• Translator Service
• Language Understanding (LUIS / Conversational Language Understanding)
• Designing Intent, Entities, and Utterances
Hands-On Lab: Create a language understanding model for customer support queries.

Module 4: Conversational AI with Azure Bot Service

• Bot Framework SDK and Composer
• Integrating LUIS into Bots
• Designing Multi-turn Conversations
• Deploying Chatbots to Microsoft Teams or Web
Hands-On Lab: Create a chatbot that answers FAQs using QnA Maker + LUIS.

Module 5: Knowledge Mining with Azure Cognitive Search

• Introduction to Azure Cognitive Search
• Data Sources (Blob Storage, SQL, Cosmos DB)
• Indexes, Indexers, Skills
• Enrichment Pipelines (OCR, Entity Recognition, Key Phrases)
• Building Searchable Knowledge Graphs
Hands-On Lab: Create a knowledge mining solution for scanning and searching PDF documents.

Module 6: Implementing Speech Solutions

• Speech-to-Text & Custom Speech Recognition
• Text-to-Speech (Neural Voices)
• Speaker Recognition
• Speech Translation Service
Hands-On Lab: Implement speech-to-text for meeting transcripts and text-to-speech with neural voice.

Module 7: Designing Responsible AI Solutions

• Responsible AI principles (Fairness, Inclusiveness, Transparency, Accountability, Reliability)
• Using Content Moderator Service
• Bias detection & model fairness considerations
• Security (Key Vault, RBAC, Private Endpoints)
Assignment: Review an AI solution for bias and propose remediation steps.

Module 8: Monitoring, Deployment, and Optimization

Azure AI resource scaling and monitoring
• Logging and tracing with Application Insights
• Deploying AI solutions as containers (Docker + Cognitive Services containers)
• CI/CD for AI models (DevOps + GitHub Actions)
• Cost optimization for AI workloads
Hands-On Lab: Deploy an Azure Cognitive Service containerized solution with Docker.

Assignments & Projects

✅ Assignments per Module (20+ total)
• Build an OCR solution to extract data from invoices.
• Implement facial verification for a secure login portal.
• Train a Custom Vision model to classify damaged products.
• Deploy a chatbot for a retail website FAQ.
• Create a sentiment analysis dashboard using Text Analytics + Power BI.

✅ Real-World Projects (Capstone)
1. Smart Retail Assistant
o Use Computer Vision to detect products from camera feeds.
o Analyze customer feedback using Text Analytics.
o Deploy a chatbot for product queries.
2. AI-Powered Document Management System
o Ingest documents into Blob Storage.
o Use OCR + Cognitive Search for knowledge mining.
o Implement chatbot + speech interface to query documents.
3. Voice-Enabled Customer Support
o Convert customer calls to text with Speech-to-Text.
o Run sentiment analysis on transcripts.
o Respond with Text-to-Speech chatbot integrated with Azure Bot Service.

Certification Exam (AI-102) Overview

• Exam Duration: ~120 minutes
• Question Types: Multiple choice, drag/drop, case studies, hands-on scenarios
• Passing Score: 700/1000
• Skills Measured:
o Plan and Manage Azure AI Solutions
o Implement Computer Vision Solutions
o Implement NLP Solutions
o Implement Conversational AI Solutions
o Implement Knowledge Mining Solutions

Deliverables

• 20+ Assignments
• 10+ Hands-on Labs
• 3 End-to-End Real-World Projects
• Exam Preparation Guide with Sample Questions

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AI-900: Microsoft Azure AI Fundamentals + AI-102: Designing And Implementing A Microsoft Azure AI Solution Course Certification With Training In Kochi

Q: AI-900: Microsoft Azure AI Fundamentals – Training Syllabus

Duration: 12 Hours Target Audience: Beginners, Business Users, Students, Non-technical professionals wanting to understand AI on Azure. Prerequisites: No prior AI/ML or programming experience required.

Q: Final Capstone Project

Build an AI-powered Customer Support System: 1. Use Azure Computer Vision to extract text from images (customer complaints). 2. Perform sentiment analysis on text using Text Analytics. 3. Build a QnA chatbot using Azure Bot Service to handle FAQs.

Q: 🎓 Certification Exam (AI-900) Overview

• Exam Duration: 45–60 minutes • Question Format: Multiple-choice, Case studies, Drag & Drop • Passing Score: 700/1000 • Skills Measured: o Describe AI workloads & Responsible AI o Describe fundamentals of ML on Azure o Describe features of Computer Vision on Azure o Describe features of NLP on Azure o Describe features of Conversational AI on Azure

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