Azure AI Fundamentals: Artificial Intelligence & Machine Learning

Azure 2020
  • 18 Videos | 1h 51m 31s
  • Includes Assessment
  • Earns a Badge
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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.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 21s
    UP NEXT
  • Playable
    2. 
    Artificial Intelligence
    5m 42s
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    3. 
    Machine Learning
    6m 57s
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    4. 
    Datasets and Data Manipulation
    4m 59s
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    5. 
    Labeled vs. Unlabeled Data
    6m 42s
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    6. 
    Features in Data
    5m 37s
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    7. 
    Machine Learning Using Regression
    6m 9s
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    8. 
    Machine Learning Using Classification
    7m 28s
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    9. 
    Machine Learning Using Clustering
    6m 52s
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    10. 
    Supervised Machine Learning
    6m 32s
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    11. 
    Unsupervised Machine Learning
    5m 44s
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    12. 
    Reliability and Safety in AI Algorithms and Use
    5m 45s
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    13. 
    Transparency in AI Algorithms and Use
    6m 16s
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    14. 
    Privacy and Security in Responsible AI
    6m 14s
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    15. 
    Inclusiveness in AI Algorithms and Use
    6m
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    16. 
    Fairness in AI Algorithms and Use
    7m 28s
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    17. 
    Accountability in AI Algorithms and Use
    6m 44s
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    18. 
    Course Summary
    1m

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