AI-900: Microsoft Azure Artificial Intelligence Fundamentals
What you'll learn
- You will be learning Fundamental concepts for Artificial Intelligence
- After taking this course you can clear the exam Microsoft Azure AI-900
- You will learn concepts on AI workloads , fundamental principles of machine learning on Azure , features of computer vision workloads on Azure
- You will learn concepts on Natural Language Processing (NLP) , features of conversational AI workloads on Azure
- Includes Hands on Lab which will help you to visualize things.
- Basic knowledge on Azure will be advantage
- Eager to learn and never ending zeal to know more about Azure AI Fundamentals
- Even if you are starting your journey to AI and Azure you can attend this course
This is the course based on latest syllabus , by attending this course you will be gaining the fundamental knowledge on Artificial Intelligence. Even if you are planning to write the exam later then also you can go through this course it will help you to understand and clear your basic for AI.
If you are looking to start your journey into the Azure then this course is for you too. You can start your journey into the cloud with Artificial Intelligence. There is no need to write any code. You need to understand the basics.
You will be taught below
Describe Artificial Intelligence workloads and considerations (15-20%)
Identify features of common AI workloads
· identify prediction/forecasting workloads
· identify features of anomaly detection workloads
· identify computer vision workloads
· identify natural language processing or knowledge mining workloads
· identify conversational AI workloads Identify guiding principles for responsible AI
· describe considerations for fairness in an AI solution
· describe considerations for reliability and safety in an AI solution
· describe considerations for privacy and security in an AI solution
· describe considerations for inclusiveness in an AI solution
· describe considerations for transparency in an AI solution
· describe considerations for accountability in an AI solution
Describe fundamental principles of machine learning on Azure (30- 35%)
Identify common machine learning types · identify regression machine learning scenarios
· identify classification machine learning scenarios
· identify clustering machine learning scenarios Describe core machine learning concepts
· identify 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 for classification and regression Identify core tasks in creating a machine learning solution
· describe common features of data ingestion and preparation
· describe common features of feature selection and engineering
· describe common features of model training and evaluation
· describe common features of model deployment and management Describe capabilities of no-code machine learning with Azure Machine Learning:
· automated Machine Learning tool
· azure Machine Learning designer
Describe features of computer vision workloads on Azure (15-20%)
Identify common types of computer vision solution:
· identify features of image classification solutions
· identify features of object detection solutions
· identify features of semantic segmentation solutions
· identify features of optical character recognition solutions
· identify features of facial detection, recognition, and analysis solutions Identify Azure tools and services for computer vision tasks
· identify capabilities of the Computer Vision service
· identify capabilities of the Custom Vision service
· identify capabilities of the Face service
· identify capabilities of the Form Recognizer service Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%) Identify features of common NLP Workload Scenarios
· identify features and uses for key phrase extraction
· identify features and uses for entity recognition
· identify features and uses for sentiment analysis
· identify features and uses for language modeling
· identify features and uses for speech recognition and synthesis
· identify features and uses for translation Identify Azure tools and services for NLP workloads
· identify capabilities of the Text Analytics service
· identify capabilities of the Language Understanding Intelligence Service (LUIS)
· identify capabilities of the Speech service
· identify capabilities of the Text Translator service
Describe features of conversational AI workloads on Azure (15-20%)
Identify common use cases for conversational AI
· identify features and uses for webchat bots
· identify features and uses for telephone voice menus
· identify features and uses for personal digital assistants Identify Azure services for conversational AI
· identify capabilities of the QnA Maker service
· identify capabilities of the Bot Framework
Who this course is for:
- Beginners who would like to make a career path in AI
- For those who want to prove the skills of AI by clearing Microsoft AI-900 Certification Exam
- If you are willing to write the exam Microsoft Certified: Azure AI Engineer Associate AI-100 in future this fundamental course will help you to clear basics.
- This course is suitable for Beginners, Intermediate , Advance level students as it start from basics.
We are working as Azure DevOps Engineer .Responsible for automated deployment process ,timely build release on UAT and PROD environment ,with 8 Years of Experience as a DevOps & Build Release Engineer and Trainer. 5000+ happy students
Cleared Azure DevOps AZ 400 (75%) and AZ 900 exams (93%)
I am MCT certified for three years . Hold 13 + certifications
• Proficient in Requirement gathering/analysis
• Effective communicator who enjoys building and maintaining client relationships
• International Experience of working with US & SPAIN Client.
• Release build and Deployment experience. Provided support in PRODUCTION/UAT Environment.
• Ability to multi-task and prioritize work as per deadlines.
• To pursue a challenging and respectable career in an organisation by learning, growing and
• Worked on AWS EC2 ,ElasticBeanStalk ,S3 ,VPC ,Migrations ,AWS services .
• Responsible for creating branches after very PSI
• Created Jenkins pipleline for daily automated deployments .
• Used powershell , python plugins in Jenkins as script for deploying applications
• Managing PRODUCTION deployments for eight applications , troubleshooting & closing
deployments under deployment window .
• Experience with CI Tools for build and deployment.
• Following an agile development environment and familiarity with Agile/SCRUM principles.
• Worked on in-built deployment tracking tool .