Microsoft Azure AI Fundamentals Certification Companion: Guide to Prepare for the AI-900 Exam

Description

Prepare for the Azure AI Fundamentals certification examination. This book covers the basics of implementing various Azure AI services in your business. The book not only helps you get ready for the AI-900 exam, but also helps you get started in the Artificial Intelligence (AI) world. The book starts with a short overview of the AI-900 exam and takes you through the exam prerequisites and the structure of the exam. You will then learn basic and advanced AI in Azure. Principles of responsible AI, Azure Machine Learning (ML), Azure Cognitive Services, and Bot Services are covered, followed by a practice test. You will go through ML fundamental concepts, model training, and validation along with case studies and a practice test for better preparation. The book includes the fundamentals of Azure and computer vision cognitive services. Various vision services and face services are demonstrated as well as analyzing image and text using OCR. You will understand concepts of natural language processing (NLP) such as text analysis, language modelling, entity recognition, sentiment analysis, speech recognition, and synthesis and also learn how to leverage Microsoft Azure for NLP. The goal of the group of technologies known as “Artificial Intelligence” (AI) is to make intelligent systems that can see, think, and help people. The goal of AI, which stands for “artificial intelligence,” is to give computers abilities that are similar to those of humans. This goal is to make computers that can do things as humans can. If AI is incorporated into the process, businesses will be able to create digital experiences that are intelligent, quick, and helpful to end users. This will give AI the ability to empower businesses. Artificial Intelligence gives businesses the chance to start over, which has the potential to change not only business processes but also whole industries and the way customers interact with businesses. The Microsoft Azure AI development framework is a powerful tool that can be used for the creation of artificial intelligence solutions in a variety of different fields. Some of these fields are conversational AI, Machine Learning, Data Sciences, robotics, the Internet of Things (IoT), and many more. After reading this book, you will be able to implement various Azure AI services and prepare for the Azure AI Fundamentals certification exam, AI-900. What Will You Learn Understand AI fundamentals and responsibilities Know the Microsoft Azure offerings for AI Understand foundational concepts for ML and Azure offerings for ML Understand Azure Cognitive Services such as Custom Vision, Face, Form Recognizer, Text-to-Speech, and Image Analysis Who This Book Is For Azure and AI users working with ML services Table of Contents About the Author About the Technical Reviewer Acknowledgments Introduction Chapter 1: Overview of AI-900 Exam Preparation Exam Overview Exam Prerequisites: Who Will Take This Examination? Taking the Exam First Thing First: Signing Up Practice Test Scheduling the Exam Choosing Your Time Block Exam Format Modules and Weightage in the Exam Module Description Module 1: Describe Artificial Intelligence Workloads and Consideration (20–25%) Lesson 1: Identify Features of Common AI Workloads Lesson 2: Identify Guiding Principles of Responsible AI Module 2: Describe Fundamental Principles of Machine Learning on Azure (25–30%) Lesson 1: Identify Common Machine Learning Types Lesson 2: Describe Core Machine Learning Concepts Lesson 3: Describe Capabilities of Visual Tools in Azure Machine Learning Studio Module 3: Describe Features of Computer Vision Workloads on Azure (15–20%) Lesson 1: Identify Common Types of Computer Vision Solutions Lesson 2: Identify Azure Tools and Services for Computer Vision Tasks Module 4: Describe Features of Natural Language Processing (NLP) Workloads on Azure (25–30%) Lesson 1: Identify Features of Common NLP Workload Scenarios Lesson 2: Identify Azure Tools and Services for NLP Workloads Lesson 3: Identify Considerations for Conversational AI Solutions on Azure Summary Chapter 2: Fundamentals of Artificial Intelligence What Is Artificial Intelligence? Strong AI Weak AI Examples of Weak AI Understanding Artificial Intelligence Workloads Machine Learning Anomaly Detection Computer Vision Natural Language Processing Knowledge Mining Principles of Responsible AI Fairness Reliability and Safety Transparency Accountability Understanding Artificial Intelligence in Microsoft Azure Data Storage Compute Compute Instances Compute Clusters Inference Clusters Attached Compute Services AI Services in Microsoft Azure Azure Machine Learning Azure Cognitive Services Azure Bot Service Azure Cognitive Search Introspective Practice Solutions to Practice Test References: Microsoft Learn Summary Chapter 3: Machine Learning Fundamental Concepts What Is Machine Learning? Core Machine Learning Concepts Dataset, Features, and Labels Dataset Training Set Validation Set Testing Set Features and Labels Machine Learning Algorithm Machine Learning Workflow Data Processing Data Modeling Deployment Model Evaluation Metrics Types of Machine Learning Supervised Machine Learning The Two Classes of Supervised Machine Learning Regression Classification Unsupervised Machine Learning Clustering The Two Important Elements: Model Training and Validation Introducing Azure Machine Learning Tools for Azure Machine Learning Azure Machine Learning Studio Azure Machine Learning Designer What Is Automated Machine Learning? Practical Labs Using Azure Machine Learning Designer to Build a Regression Model Create Azure Machine Learning Workspace Create Compute Create Pipeline in Designer Add and Explore a Dataset Add Data Transformations Cleaning Training Our Model Scoring Model Evaluation Submission Scored Labels Evaluation Result Exploration Delete Resources Introspective Practice Solutions to the Practice Test References: Microsoft Learn Summary Chapter 4: Computer Vision Getting Started with Azure Cognitive Services Benefits of Cognitive Services Azure Cognitive Services Speech Language Vision Decision OpenAI Service What Is Computer Vision Computer Vision Core Elements: Image Classification and Object Detection Image Classification Object Detection Computer Vision Application Semantic Segmentation Image Analysis Optical Character Recognition (OCR) Exploring Various Vision Services Computer Vision Detecting Object Detect Texts Categorizing an Image Describe the Image Detecting Faces Detect the Color Scheme Get the Area of Interest Custom Vision Image Classification Using the Azure Custom Vision Service Object Detection Using Azure Custom Vision Service Face Identifying Faces in a Group Identifying Similar Faces Face Detection Emotion Recognition Face Grouping Form Recognizer Simple Text Extraction Customized Results Flexible Deployment Built-In Security Understanding of Optical Character Reader Practical Labs Computer Vision API – Text Extraction Create Computer Vision Resource Connect a Console App to Computer Vision Resource Introspective Practice Test Solutions for the Practice Test References: Microsoft Learn Summary Untitled Chapter 5: Fundamentals of Natural Language Processing Getting Started with Natural Language Processing What Is Natural Language Processing? What Are the Business Applications of NLP? How Does NLP Function? Stages of Natural Language Processing (NLP) Core NLP Responsibilities Text Analysis and Entity Recognition Text Analysis Entity Recognition Organize Tickets in Customer Support Learn from Customer Feedback Content Suggestion Resumes of Processes Sentiment Analysis Indeterminate Sentiment Speech Recognition and Synthesis Speech Recognition Speech Synthesis Mobile Phones Word Processing Software Education Customer Care Applications in Healthcare Reporting in Court Recognizing Emotions Hands-Free Communication Is Possible Machine Translation The Use of Rules in Machine Translation Statistics in Machine Translation Technology Semantic Language Modeling AI for Conversational Interactions Advantages of Conversational AI for Businesses Improve Client Service Drive Marketing and Sales Initiatives Improve Agent Skills Reduce Response Times Personalize the Customer Experience Microsoft Azure for NLP Core Azure NLP Workloads: Language, Speech, and Translator Language Language Detection Key Phrase Extraction Entity Detection Sentiment Analysis Brand Monitoring with Sentiment Analysis Market Research and Analysis Using Sentiment Analysis Question Answering Conversational Language Understanding Utterances Entities Intents Speech Text-to-Speech Speech-to-Text Speech Translation Translator Literal and Semantic Translation Text and Speech Translation Microsoft Azure Platform for Conversational AI Azure Bot Service Develop a Knowledge Base Custom Question Answering Test the Knowledge Base Extend and Customize the Bot Join Channels Practical Labs Creating a Custom Question-Answering Knowledge Base Editing Your Knowledge Base Training and Testing the Knowledge Base Creating an Informational Bot for the Knowledge Base Introspective Test Solutions to the Practice Test References: Microsoft Learn Summary Index Capture.PNG Capture.PNG