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Azure AI Fundamentals Certification (AI-900) - Full Course to PASS the Exam
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- 1 Introduction
- 2 Exam Guide Breakdown
- 3 Layers of Machine Learning
- 4 Key Elements of AI
- 5 DataSets
- 6 Labeling
- 7 Supervised and Unsupervised Reinforcement
- 8 Neural Networks and Deep Learning
- 9 GPU
- 10 CUDA
- 11 Simple ML Pipeline
- 12 Forecast vs Prediction
- 13 Metrics
- 14 Juypter Notebooks
- 15 Regression
- 16 Classification
- 17 Clustering
- 18 Confusion Matrix
- 19 Anomaly Detection AI
- 20 Computer Vision AI
- 21 Natural Language Processing AI
- 22 Conversational AI
- 23 Responsible AI
- 24 Fairness
- 25 Reliability and safety
- 26 Privacy and security
- 27 Inclusiveness
- 28 Transparency
- 29 Accountability
- 30 Guidelines for Human AI Interaction
- 31 Follow Along Guidelines for Human AI Interaction
- 32 Azure Cognitive Services
- 33 Cognitive API Key and Endpoint
- 34 Knowledge Mining
- 35 Face Service
- 36 Speech and Translate Service
- 37 Text Analytics
- 38 OCR Computer Vision
- 39 Form Recognizer
- 40 Form Recognizer Custom Models
- 41 Form Recognizer Prebuilt Models
- 42 LUIS
- 43 QnA Maker
- 44 Azure Bot Service
- 45 Azure Machine Learning Service
- 46 Studio Overview
- 47 Studio Compute
- 48 Studio Data Labeling
- 49 Data Stores
- 50 Datasets
- 51 Experiments
- 52 Pipelines
- 53 ML Designer
- 54 Model Registry
- 55 Endpoints
- 56 Notebooks
- 57 Introduction to AutoML
- 58 Data Guard Rails
- 59 Automatic Featurization
- 60 Model Selection
- 61 Explanation
- 62 Primary Metrics
- 63 Validation Type
- 64 Introduction to Custom Vision
- 65 Project Types and Domains
- 66 Custom Vision Features
- 67 Setup
- 68 Computer Vision
- 69 Custom Vision Classification
- 70 Custom Vision Object Detection
- 71 Face Service
- 72 Form Recognizer
- 73 OCR
- 74 Text Analysis
- 75 QnAMaker
- 76 LUIS
- 77 AutoML
- 78 Designer
- 79 MNIST
- 80 Data Labeling
- 81 Clean up
- 82 AI CheatSheet
