Azure AI Fundamentals Certification (AI-900) - Full Course to PASS the Exam

Embark on a comprehensive course designed to fully prepare you for the Azure AI Fundamentals Certification (AI-900) exam. Dive deep into the world of artificial intelligence and machine learning, covering essential topics such as ML introduction, common AI workloads, responsible AI practices, and Azure Cognitive Services. Explore the intricacies of ML Studio, AutoML, and Custom Vision through hands-on follow-along sessions. Master key concepts including neural networks, deep learning, GPU usage, and various AI applications like anomaly detection, computer vision, and natural language processing. Gain practical experience with Azure services such as Face Service, Form Recognizer, and LUIS. Benefit from a detailed exam guide breakdown, an AI cheatsheet, and expert instruction to confidently approach and pass the certification exam.

Introduction.
Exam Guide Breakdown.
Layers of Machine Learning.
Key Elements of AI.
DataSets.
Labeling.
Supervised and Unsupervised Reinforcement.
Neural Networks and Deep Learning.
GPU.
CUDA.
Simple ML Pipeline.
Forecast vs Prediction.
Metrics.
Juypter Notebooks.
Regression.
Classification.
Clustering.
Confusion Matrix.
Anomaly Detection AI.
Computer Vision AI.
Natural Language Processing AI.
Conversational AI.
Responsible AI.
Fairness.
Reliability and safety.
Privacy and security.
Inclusiveness.
Transparency.
Accountability.
Guidelines for Human AI Interaction.
Follow Along Guidelines for Human AI Interaction.
Azure Cognitive Services.
Cognitive API Key and Endpoint.
Knowledge Mining.
Face Service.
Speech and Translate Service.
Text Analytics.
OCR Computer Vision.
Form Recognizer.
Form Recognizer Custom Models.
Form Recognizer Prebuilt Models.
LUIS.
QnA Maker.
Azure Bot Service.
Azure Machine Learning Service.
Studio Overview.
Studio Compute.
Studio Data Labeling.
Data Stores.
Datasets.
Experiments.
Pipelines.
ML Designer.
Model Registry.
Endpoints.
Notebooks.
Introduction to AutoML.
Data Guard Rails.
Automatic Featurization.
Model Selection.
Explanation.
Primary Metrics.
Validation Type.
Introduction to Custom Vision.
Project Types and Domains.
Custom Vision Features.
Setup.
Computer Vision.
Custom Vision Classification.
Custom Vision Object Detection.
Face Service.
Form Recognizer.
OCR.
Text Analysis.
QnAMaker.
LUIS.
AutoML.
Designer.
MNIST.
Data Labeling.
Clean up.
AI CheatSheet.