SkillSoft Explore Course – Lambers, Inc.

IT Skills Cloud Computing and Virtualization Microsoft AI-900: Azure AI Fundamentals

Artificial Intelligence and machine learning in particular are solving a significant number of business and social problems and giving computers a new way to handle and process vast amounts of data. In this course, you'll learn about AI and machine learning concepts regarding regression, classification, and clustering algorithms. You'll explore how to manage datasets and work with labeled versus unlabeled data. You'll learn how supervised and unsupervised machine learning can be used, as well as how to build and use AIs safely, transparently, and fairly. This course is one of a collection that prepares learners for the Microsoft Azure AI Fundamentals (AI-900) exam.

Asset ID	it_clazaif_01_enus
Course Type	Video Course
Course Category	Cloud Computing and Virtualization

Objectives
AI-900: Azure AI Fundamentals: : Artificial Intelligence & Machine Learning discover the key concepts covered in this course describe Artificial Intelligence and how it can be used to solve business problems describe machine learning and how it can be used for anomaly detection, computer vision, and natural language processing describe datasets and how to manipulate data for those datasets differentiate between labeled and unlabeled data and describe why some AI models require labeled data describe how features are selected and used from datasets in AI algorithms describe regression algorithms and how they are used to make predictions describe classification algorithms and how they are used to classify objects or relations describe clustering algorithms and how they can be used to determine groupings in data describe how supervised machine learning models use labeled data, are simpler to build, and have more accurate results describe how unsupervised machine learning models use unlabeled data, which makes them more complex but more flexible than supervised machine learning describe how to responsibly use AI by making sure it is reliable and safe describe how transparency should be used with AI algorithms in a responsible way describe how privacy and security must be factored into responsibly creating and using AI solutions describe how the use of inclusiveness in AI algorithms can benefit everyone describe how fairness in AI algorithms results in responsible AI describe how governance and organizational policies provide accountability for AI responsibility summarize the key concepts covered in this course

Objectives

AI-900: Azure AI Fundamentals: : Artificial Intelligence & Machine Learning

discover the key concepts covered in this course
describe Artificial Intelligence and how it can be used to solve business problems
describe machine learning and how it can be used for anomaly detection, computer vision, and natural language processing
describe datasets and how to manipulate data for those datasets
differentiate between labeled and unlabeled data and describe why some AI models require labeled data
describe how features are selected and used from datasets in AI algorithms
describe regression algorithms and how they are used to make predictions
describe classification algorithms and how they are used to classify objects or relations
describe clustering algorithms and how they can be used to determine groupings in data
describe how supervised machine learning models use labeled data, are simpler to build, and have more accurate results
describe how unsupervised machine learning models use unlabeled data, which makes them more complex but more flexible than supervised machine learning
describe how to responsibly use AI by making sure it is reliable and safe
describe how transparency should be used with AI algorithms in a responsible way
describe how privacy and security must be factored into responsibly creating and using AI solutions
describe how the use of inclusiveness in AI algorithms can benefit everyone
describe how fairness in AI algorithms results in responsible AI
describe how governance and organizational policies provide accountability for AI responsibility
summarize the key concepts covered in this course