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AI-900: Microsoft Azure Artificial Intelligence Fundamentals

Develop essential data science & ai skills with expert instruction and practical examples.

Online Course
Self-paced learning
Flexible Schedule
Learn at your pace
Expert Instructor
Industry professional
Certificate
Upon completion
What You'll Learn
Master the fundamentals of data science & ai
Apply best practices and industry standards
Build practical projects to demonstrate your skills
Understand advanced concepts and techniques

Skills you'll gain:

Professional SkillsBest PracticesIndustry Standards
Prerequisites & Target Audience

Skill Level

IntermediateSome prior knowledge recommended

Requirements

Basic understanding of data science & ai
Enthusiasm to learn
Access to necessary software/tools
Commitment to practice

Who This Course Is For

Professionals working in data science & ai
Students and career changers
Freelancers and consultants
Anyone looking to improve their skills
Course Information

About 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 Skills MeasuredDescribe 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 solutionDescribe 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 designerDescribe 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 serviceDescribe 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.

Provider
Udemy
Estimated Duration
10-20 hours
Language
English
Category
Technology & Programming

Topics Covered

Data Science & AI

Course Details

Format
Online, Self-Paced
Access
Lifetime
Certificate
Upon Completion
Support
Q&A Forum
Course Details
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This course includes:

Lifetime access to course content
Access on mobile and desktop
Certificate of completion
Downloadable resources

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