AI-900: Microsoft Azure Artificial Intelligence Fundamentals
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 13 lectures (3h 50m) | Size: 847.4 MB

With Demos and Practice test learn basics of Azure Artificial Intelligence and get certified with this complete course
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.

Requirements
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

Description
This is the course based on the 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 Measured

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 typesidentify 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.

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