Exam AI-900 Microsoft Azure AI Fundamentals (Video)
- ByTim Warner
- Published Aug 2, 2023 byMicrosoft Press.
Online Video
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Description
- Copyright 2024
- Edition: 1st
- Online Video
- ISBN-10: 0-13-820284-2
- ISBN-13: 978-0-13-820284-2
5+ Hours of Video Instruction
Prepare to become an AI professional and ace the Microsoft Exam AI-900 with our comprehensive video course from Microsoft Press, covering everything from the fundamentals of AI to practical applications using Azure AI services.
The technologies covered in Microsoft Exam AI-900, including machine learning, natural language processing, computer vision, and cognitive services, are widely used in real-world applications across various industries.
在医疗、人工智能用于医学图像分析is, drug discovery, and predicting patient outcomes. In finance, AI is used for fraud detection, credit scoring, and algorithmic trading. In retail, AI is used for recommendation systems, supply chain optimization, and inventory management. In manufacturing, AI is used for predictive maintenance, quality control, and autonomous robotics. In customer service, AI is used for chatbots, sentiment analysis, and personalized marketing.
These are just a few examples of how AI technologies are being used in the real world, and the demand for professionals with the skills to develop and implement these technologies is rapidly increasing.
Skill Level:
- Beginner
- Intermediate
What You Will Learn:
完成这个视频之后,你将能够:
- Comprehend Artificial Intelligence workloads and considerations
- Understand fundamental principles of machine learning on Azure
- Work with all features of computer vision workloads on Azure
- Experiment with features of Natural Language Processing (NLP) workloads on Azure
Who Should Take This Course:
- Data analysts or scientists who want to expand their skills to include AI and machine learning.
- Software developers or engineers who want to incorporate AI capabilities into their software applications.
- IT professionals who want to learn how to leverage AI technologies in their organization.
- Project managers or team leaders who want to understand the potential impact of AI on their organization.
- Students or recent graduates who want to gain a basic understanding of AI concepts and how they can be applied in different industries.
Prerequisite:
This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, awareness of cloud basics and client-server applications would be beneficial.
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Sample Content
Table of Contents
Introduction
Lesson 1: Identify Features of Common AI Workloads
1.1 Identify features of anomaly detection workloads
1.2 Identify computer vision workloads
1.3 Identify natural language processing workloads
1.4 Identify knowledge mining workloads
Lesson 2: Identify Guiding Principles for Responsible AI
2.1 Describe considerations for fairness in an AI solution
2.2 Describe considerations for reliability and safety in an AI solution
2.3 Describe considerations for privacy and security in an AI solution
2.4 Describe considerations for inclusiveness in an AI solution
2.5 Describe considerations for transparency in an AI solution
2.6 Describe considerations for accountability in an AI solution
Lesson 3: Identify Common Machine Learning Types
3.1 Identify regression machine learning scenarios
3.2 Identify classification machine learning scenarios
3.3 Identify clustering machine learning scenarios
Lesson 4: Describe Core Machine Learning Concepts
4.1 Identify features and labels in a dataset for machine learning
4.2 Describe how training and validation datasets are used in machine learning
Lesson 5: Describe Capabilities of Visual Tools in Azure Machine Learning Studio
5.1 Automated machine learning
5.2 Azure Machine Learning designer
Lesson 6: Identify Common Types of Computer Vision Solution
6.1 Identify features of image classification solutions
6.2 Identify features of object detection solutions
6.3 Identify features of optical character recognition solutions
6.4 Identify features of facial detection and facial analysis solutions
Lesson 7: Identify Azure Tools and Services for Computer Vision Tasks
7.1 Identify capabilities of the Computer Vision service
7.2 Identify capabilities of the Custom Vision service
7.3 Identify capabilities of the Face service
7.4 Identify capabilities of the Form Recognizer service
Lesson 8: Identify Features of Common NLP Workload Scenarios
8.1 Identify features and uses for key phrase extraction
8.2 Identify features and uses for entity recognition
8.3 Identify features and uses for sentiment analysis
8.4 Identify features and uses for language modeling
8.5 Identify features and uses for speech recognition and synthesis
8.6 Identify features and uses for translation
Lesson 9: Identify Azure Tools and Services for NLP Workloads
9.1 Identify capabilities of the Language service
9.2 Identify capabilities of the Speech service
9.3 Identify capabilities of the Translator service
Lesson 10: Identify Considerations for Conversational AI Solutions on Azure
10.1 Identify features and uses for bots
10.2 Identify capabilities of the Power Virtual Agents and Azure Bot service
Summary