Artificial Intelligence Project

Service Description

Microsoft Azure AI-900: AI Fundamentals – Study Guide Comprehensive guide for preparing the Microsoft AI-900 certification exam. 1. Overview of AI-900 Exam • Exam Code: AI-900 • Certification: Microsoft Certified – Azure AI Fundamentals • Focus: Fundamental knowledge of AI and its implementation using Azure services • Ideal for: Beginners, students, and professionals exploring AI concepts 2. Core AI Concepts • Definition and types of Artificial Intelligence (Narrow, General, Super AI) • Difference between AI, Machine Learning, and Deep Learning • Common AI workloads: Machine Learning, Computer Vision, Natural Language Processing, Conversational AI • Examples of AI in daily life – recommendation systems, chatbots, image recognition, and fraud detection 3. Machine Learning Fundamentals • Supervised Learning – trained on labeled data (Regression, Classification) • Unsupervised Learning – patterns from unlabeled data (Clustering) • Reinforcement Learning – reward-based learning for agents • Model training, validation, and evaluation using metrics like accuracy, precision, recall, F1-score • Overfitting vs Underfitting concepts • Azure ML Studio: No-code/low-code environment for ML model creation and deployment 4. Computer Vision • Understanding image classification, object detection, and facial recognition • Azure Cognitive Services – Computer Vision API, Face API, Custom Vision • Image tagging, Optical Character Recognition (OCR), and scene analysis 5. Natural Language Processing (NLP) and Conversational AI • Processing human language – speech and text • Azure Language Service – sentiment analysis, key phrase extraction, translation • Conversational AI – Azure Bot Service and Language Understanding (LUIS) • Speech Services – speech recognition and synthesis (Text-to-Speech) 6. Responsible AI Principles • Fairness – models should not create bias • Reliability & Safety – consistent and accurate outputs • Privacy & Security – protecting sensitive data • Inclusiveness – accessible and unbiased AI for all users • Transparency & Accountability – clear model explainability