Microsoft's AI-900 Exam on Azure AI Fundamentals tests your machine learning knowledge, AI concepts, and associated Microsoft Azure services. This exam's participants must be thoroughly familiar with Microsoft's Exam AI-900 course materials. This course from MyAssignmenthelp.com will prepare learners for the rigours of the exam through a comprehensive & well-structured curriculum.
Completing this course will not just help learners score excellent grades in the MS AI-900 exam but develop strong fundamentals in artificial intelligence and machine learning. Here are its most prominent features:
- Hands-On & Instructor-Led – Dig deep into the Microsoft AI-9000 course and the exam’s fundamentals
- Designed For Absolute Beginners
- Exhaustive Guidance For the Microsoft AI-900: Azure AI Fundamentals Examination
- 2+ Practice Tests, PPTs, Demos, Etc.
- More than 8 hours of on-demand videos
Why Enrol In MyAssignmenthelp.com’s Azure AI Fundamentals Course?
AI and machine learning are taking the world by storm. With widespread integrations across almost every business sector, they are highly lucrative career options for tech students & professionals.
AI on Cloud is the next big thing. Google Cloud, Amazon Web Services, and Microsoft Azure are three of the most popular cloud computing & AI-as-a-Service platform today. Mastering any one of these platforms will elevate your skills and unlock a whole lot of opportunities.
Completing MyAssignmenthelp.com’s AI-900 Azure AI Fundamentals examination course will help you score superb grades and grab a certificate from Microsoft, one of the world’s largest tech companies. In addition, the course will impart skills and knowledge to prepare you for subsequent Azure-based certifications such as Azure Data Scientist or an Azure AI Engineer Associate.
How Will The AI-900 Azure, AI Fundamentals Course, Help Your Career?
Mastery over AI-900 Azure AI Fundamentals will boost your skills, expand your knowledge, and enable you to:
- Gain a fundamental understanding of Azure Cloud
- Demonstrate ideas & knowledge about AI workloads & considerations
- Be thorough with the basics of machine learning on Azure Cloud
- Understand the features of computer vision workloads on Azure
- Become fluent with natural language processing workloads on Azure
- Develop clear-cut ideas about conversational workloads on the Cloud
- Design no-code predictive models in Azure
- Learn about all the varying machine learning algorithms
AI Workloads & Considerations You’ll Learn
This course will give you prominent ideas of different AI workloads & their considerations.
Here are some ideas about the AI workloads/workflows you will learn more about.
- Anomaly detection workloads in containers.
- Microsoft’s Seeing AI, Azure Cognitive Services (computer vision),
- The Language Understanding Intelligent Service (LUIS, a conversational AI that uses Natural Language Processing and the like)
- Classification of images using Azure, design custom vision, & face and object detection systems
- Learning in-depth about the Azure Face Service operations
- Text recognition workflows using the Azure Computer Vision services
- Computer Vision, Custom Image Classification, Face Detection & Recognition, & Optical Character Recognition From Forms & Invoices
- In-Depth Text Analytics (Sentiment Analysis, Entity Detection, Extract Key Words & Phrases)
- Text Translation, Speech Analysis, Synthesis & Translation
- Using LUIS to train language models for different workloads.
- Learning how to create, deploy, and manage conversational AI bots using the Azure Bot Service
- Understanding the importance of fairness, privacy, reliability, security, transparency, and accountability in AI model development & operations
Features of Computer Vision Workloads on Azure
Computer vision is a sub-domain in AI wherein models are designed to detect, classify, and perceive entities in the visual domain. Microsoft Azure offers cloud computer vision services that discover & analyse content from images & videos.
- Computer Vision Workloads on Azure help transform & streamline business processes. Companies will be able to implement powerful robotic process automation APIs. These models can identify more than 10000 objects & analyse concepts from multiple images & document types, and even engage in digital asset management strategies.
- Typical Computer Vision workloads include automated text extraction using optical character recognition, object identification, spatial analyses, and understanding of different kinds of images. The computer vision services are a part of Azure Cognitive Services and boast flexible deployment in the Cloud or on edge through containers.
- Design and deploy computer vision on-premises or in the Cloud. Embed Microsoft Computer Vision easily across diverse scenarios & workloads.
Who Is This Course For? What Will You Learn From This Course?
You will need to ace MyAssignmenthelp.com’s AI-900 Azure AI Fundamentals course if you want top Microsoft’s AI-900 Azure Fundamentals examination.
This course can also be a great starting point for anyone looking to:
- Learn the basics of Azure Cloud and Azure AI
- Learn the fundamentals of Artificial Intelligence & Machine Learning
- Become thoroughly acquainted with AI workloads on computer vision, text analytics & Natural Language Processing, conversational AI, speech analysis, and object & face detection workloads on Azure Cloud AI
- Find out about generic AI workflows, right from conceptualisation & design to development, deployment, and operations.
MAH’s course will help learners develop a strong foundation in AI, ML, and critical business applications through:
- Two practice tests
- 4 BONUS Quizzes
- Downloadable Course Materials
- Lucid & Informative Presentations
- 8+ hours of On-Demand Videos
- Demos and much more
Know Your Instructor
Eshant Garg is an Azure-certified DevOps engineer with more than 12 years of industry experience. A software and ML engineer at a global corporation in the UK, he is the brains behind this course and numerous other online courses on cloud-native services, AI & ML, APIs, containers, microservices, serverless architectures, and more.