Azure AI Fundamentals: Artificial Intelligence & Machine Learning

Azure    |    Beginner
  • 18 videos | 1h 43m 31s
  • Includes Assessment
  • Earns a Badge
Rating 4.4 of 172 users Rating 4.4 of 172 users (172)
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

  • 1m 21s
  • 5m 42s
    In this video, you will learn how to describe Artificial Intelligence and how it can be used to solve business problems. FREE ACCESS
  • Locked
    3.  Machine Learning
    6m 57s
    During this video, you will learn how to describe machine learning and how it can be used for anomaly detection, computer vision, and natural language processing. FREE ACCESS
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    4.  Datasets and Data Manipulation
    4m 59s
    In this video, you will learn how to describe datasets and how to manipulate data for those datasets. FREE ACCESS
  • Locked
    5.  Labeled vs. Unlabeled Data
    6m 42s
    Discover how to differentiate between labeled and unlabeled data and describe why some AI models require labeled data. FREE ACCESS
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    6.  Features in Data
    5m 37s
    In this video, you will discover how features are selected and used from datasets in AI algorithms. FREE ACCESS
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    7.  Machine Learning Using Regression
    6m 9s
    After completing this video, you will be able to describe regression algorithms and how to use them to make predictions. FREE ACCESS
  • Locked
    8.  Machine Learning Using Classification
    7m 28s
    In this video, you will learn how to describe classification algorithms and how they are used to classify objects or relations. FREE ACCESS
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    9.  Machine Learning Using Clustering
    6m 52s
    Upon completion of this video, you will be able to describe clustering algorithms and how they can be used to determine groupings in data. FREE ACCESS
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    10.  Supervised Machine Learning
    6m 32s
    During this video, you will learn how to describe how supervised machine learning models use labeled data, are simpler to build, and have more accurate results. FREE ACCESS
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    11.  Unsupervised Machine Learning
    5m 44s
    How do unsupervised machine learning models use unlabeled data? FREE ACCESS
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    12.  Reliability and Safety in AI Algorithms and Use
    5m 45s
    Learn how to describe how to use AI responsibly by making sure it is reliable and safe. FREE ACCESS
  • Locked
    13.  Transparency in AI Algorithms and Use
    6m 16s
    In this video, you will learn how to describe how transparency should be used with AI algorithms in a responsible way. FREE ACCESS
  • Locked
    14.  Privacy and Security in Responsible AI
    6m 14s
    During this video, you will learn how to describe how privacy and security must be factored into responsibly creating and using AI solutions. FREE ACCESS
  • Locked
    15.  Inclusiveness in AI Algorithms and Use
    6m
    In this video, discover how the use of inclusiveness in AI algorithms can benefit everyone. FREE ACCESS
  • Locked
    16.  Fairness in AI Algorithms and Use
    7m 28s
    Discover how to describe how fairness in AI algorithms results in responsible AI. FREE ACCESS
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    17.  Accountability in AI Algorithms and Use
    6m 44s
    In this video, you will discover how governance and organizational policies provide accountability for AI responsibility. FREE ACCESS
  • Locked
    18.  Course Summary
    1m
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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