AI-900 - Azure AI Fundamentals: Artificial Intelligence Concepts





Artificial intelligence (AI) and machine learning (ML) are expansive concepts that mean different things to different people. With a vast and ever-growing list of practical applications for AI/ML, it is no surprise that the technology is garnering the attention of organizations far and wide. In this course, you will explore key concepts of AI, beginning with ML types. Then you will discover data in ML, labeled and unlabeled data, and data features. Next, you will delve into key methods and techniques, such as regression, binary classification, multi-class classification, and clustering. Finally, you will focus on the features, advantages, and disadvantages of supervised and unsupervised learning, and the purpose of deep learning. This course is one of a collection that prepares learners for the Microsoft Azure AI Fundamentals (AI-900) exam.




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AI-900 - Azure AI Fundamentals: Artificial Intelligence Concepts

  • provide an overview of typical ML types
  • outline key considerations for datasets and data manipulation in ML
  • provide an overview of labeled and unlabeled data and their purpose in ML applications
  • describe how features are selected and used from datasets in artificial intelligence (AI) algorithms
  • provide an overview of regression algorithms and their role in ML
  • outline binary classification, multi-class, multi-label, and imbalanced algorithms and their roles in classifying objects or relationships in ML
  • provide an overview of clustering algorithms and how they can be used to determine groupings in data
  • describe key features, benefits, and drawbacks of supervised ML models
  • describe key features, benefits, and drawbacks of unsupervised ML models
  • outline the purpose and features of deep learning algorithms


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