(Ebook PDF) Exam Ref AI 900 Microsoft Azure AI Fundamentals 1st edition by Julian Sharp 0137358148 9780137358144 full chapters

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ISBN-10 :  0137358148 

ISBN-13 :  9780137358144

Author:   Julian Sharp 

Prepare for Microsoft Exam AI-900 and help demonstrate your real-world knowledge of diverse machine learning (ML) and artificial intelligence (AI) workloads, and how they can be implemented with Azure AI. Designed for business stakeholders, new and existing IT professionals, consultants, and students, this Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the Microsoft Certified: Azure AI Fundamentals level.   Focus on the expertise measured by these objectives: • Describe AI workloads and considerations • Describe fundamental principles of machine learning on Azure • Describe features of computer vision workloads on Azure • Describe features of Natural Language Processing (NLP) workloads on Azure • Describe features of conversational AI workloads on Azure This Microsoft Exam Ref: • Organizes its coverage by exam objectives • Features strategic, what-if scenarios to challenge you • Assumes you are a business user, stakeholder, technical professional, or student who wants to become familiar with Azure AI; requires no data science or software engineering experience. 

Exam Ref AI-900 Microsoft Azure AI Fundamentals 1st Table of contents:

Chapter 1 Describe Artificial Intelligence workloads and considerations
Skill 1.1: Identify features of common AI workloads
Describe Azure services for AI and ML
Understand Azure Machine Learning
Understand Azure Cognitive Services
Describe the Azure Bot Service
Identify common AI workloads
Skill 1.2: Identify guiding principles for Responsible AI
Describe the Fairness principle
Describe the Reliability & Safety principle
Describe the Privacy & Security principle
Describe the Inclusiveness principle
Describe the Transparency principle
Describe the Accountability principle
Understand Responsible AI for Bots
Understand Microsoft’s AI for Good program
Chapter summary
Thought experiment
Thought experiment answers
Chapter 2 Describe fundamental principles of machine learning on Azure
Skill 2.1: Identify common machine learning types
Understand machine learning model types
Describe regression models
Describe classification models
Describe clustering models
Skill 2.2: Describe core machine learning concepts
Understand the machine learning workflow
Identify the features and labels in a dataset for machine learning
Describe how training and validation datasets are used in machine learning
Describe how machine learning algorithms are used for model training
Select and interpret model evaluation metrics
Skill 2.3: Identify core tasks in creating a machine learning solution
Understand machine learning on Azure
Understand Azure Machine Learning studio
Describe data ingestion and preparation
Describe feature selection and engineering
Describe model training and evaluation
Describe model deployment and management
Skill 2.4: Describe capabilities of no-code machine learning with Azure Machine Learning
Describe Azure Automated Machine Learning
Describe Azure Machine Learning designer
Chapter summary
Thought experiment
Thought experiment answers
Chapter 3 Describe features of computer vision workloads on Azure
Skill 3.1: Identify common types of computer vision solution
Introduce Cognitive Services
Understand computer vision
Describe image classification
Describe object detection
Describe optical character recognition
Describe facial detection, recognition, and analysis
Skill 3.2: Identify Azure tools and services for computer vision tasks
Understand the capabilities of the Computer Vision service
Understand the Custom Vision service
Understand the Face service
Understand the Form Recognizer service
Chapter summary
Thought experiment
Thought experiment answers
Chapter 4 Describe features of Natural Language Processing (NLP) workloads on Azure
Skill 4.1: Identify features of common NLP workload scenarios
Describe Natural Language Processing
Describe language modeling
Describe key phrase extraction
Describe named entity recognition
Describe sentiment analysis
Describe speech recognition and synthesis
Describe translation
Skill 4.2: Identify Azure tools and services for NLP workloads
Identify the capabilities of the Text Analytics service
Identify the capabilities of the Language Understanding service (LUIS)
Identify the capabilities of the Speech service
Identify the capabilities of the Translator service
Chapter summary
Thought experiment
Thought experiment answers
Chapter 5 Describe features of conversational AI workloads on Azure
Skill 5.1: Identify common use cases for conversational AI
Identify features and uses for webchat bots
Identify common characteristics of conversational AI solutions
Skill 5.2: Identify Azure services for conversational AI
Identify capabilities of the QnA Maker service
Identify capabilities of the Azure Bot Service
Chapter summary
Thought experiment
Thought experiment answers

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