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Data Labeling for Machine Learning

سرفصل های دوره

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.

This course was created by Janani Ravi. We are pleased to host this training in our library.


01 - Introduction
  • 01 - The need for data labeling

  • 02 - 1. Get Started with Data Labeling
  • 01 - The data labeling process
  • 02 - Approaches to data labeling
  • 03 - Data labeling challenges, best practices, and use cases
  • 04 - Data labeling with Azure ML
  • 05 - Setting up an Azure ML workspace
  • 06 - Setting up an image labeling project Creating data assets
  • 07 - Setting up an image labeling project Configuring settings
  • 08 - Manual image labeling and review
  • 09 - Manual labeling progress checks

  • 03 - 2. Perform Manual and ML-Assisted Data Labeling on Azure
  • 01 - Automated machine learning for image classification
  • 02 - Examining model training metrics
  • 03 - Data labeling project insights
  • 04 - ML assisted labeling with clustering and pre-labeling
  • 05 - Configuring inference for new training runs
  • 06 - Exploring the labeled dataset

  • 04 - 3. Use Snorkel for Data Labeling
  • 01 - Programmatic labeling with Snorkel
  • 02 - Installing Python libraries
  • 03 - Exploring the spam ham dataset
  • 04 - Writing and analyzing labeling functions
  • 05 - Exploring other labeling functions
  • 06 - Programmatic labeling using the majority label voter
  • 07 - Scoring and comparing the label models

  • 05 - 4. Create Diverse Labeling Functions and Models in Snorkel
  • 01 - Increasing the number of labeling functions
  • 02 - Using sentiment and parts of speech tagging in labeling functions
  • 03 - Evaluating labeling function metrics on test data
  • 04 - Using all labeling functions to label data
  • 05 - Training a classifier on programmatically generated labels

  • 06 - Conclusion
  • 01 - Summary and next steps
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