Azure Data Scientist Associate: Machine Learning

Azure 2021    |    Intermediate
  • 11 Videos | 1h 12m 27s
  • Includes Assessment
  • Earns a Badge
Machine Learning uses real data to train algorithms that can be used for anomaly detection, computer vision, and natural language processing. In this course, you'll learn about datasets and how to manipulate data for them. Next, you'll learn the difference between labeled and unlabeled data and why some AI models require labeled data. You'll examine the features that should be used for a selected dataset. Next, you'll learn about the types of machine learning algorithms that are available, including regression algorithms, classification algorithms, and clustering algorithms. Finally, you'll explore the difference between supervised and unsupervised machine learning models. This course is one in a collection that prepares learners for the Designing and Implementing a Data Science Solution on Azure (DP-100) exam.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    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
    describe the difference between labeled and unlabeled data and 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 discover patterns from unlabelled data and can perform complex processing tasks
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 41s
    UP NEXT
  • Playable
    2. 
    Machine Learning
    7m 55s
  • Locked
    3. 
    Data Manipulation and Datasets
    7m 54s
  • Locked
    4. 
    Labeled and Unlabeled Data
    5m 26s
  • Locked
    5. 
    Data Features
    8m 35s
  • Locked
    6. 
    Machine Learning Regression Algorithms
    7m 20s
  • Locked
    7. 
    Machine Learning Classification Algorithms
    6m 27s
  • Locked
    8. 
    Machine Learning Clustering Algorithms
    6m 43s
  • Locked
    9. 
    Supervised Machine Learning
    7m 53s
  • Locked
    10. 
    Unsupervised Machine Learning
    7m 19s
  • Locked
    11. 
    Course Summary
    44s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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