Data Labeling for Machine Learning | LinkedIn

Almost 2.5 quintillion bytes of data are produced every day—mostly raw, unlabeled data—but supervised learning techniques for machine learning require data to be labeled in order to use it for training. This makes data labeling, time-consuming and expensive though it may be, a vital part of machine learning. In this course, certified Google cloud architect and data engineer Janani Ravi guides you through how to get started with data labeling. Learn about different approaches to data labeling, as well as the challenges, best practices, and use cases that go with it. Go over data labeling with Azure ML, and find out how to set up an image labeling project and perform manual image labeling, reviews, and progress checks. Step through the full process of performing manual and ML-assisted data labeling on Azure, then explore how to use Snorkel for data labeling, including how to create diverse labeling functions and models.

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