IT Professional Curricula     Internet and Network Technologies Solution Area     Cloud Computing     DP-100: Designing and Implementing a Data Science Solution on Azure

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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.

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Objectives

DP-100 - Azure Data Scientist Associate: Machine Learning 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