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Executive Guide to Human-in-the-Loop Machine Learning and Data Annotation

Executive Guide to Human-in-the-Loop Machine Learning and Data Annotation

1h 2mBeginner2023-12-12

Authors

Keith McCormick

Keith McCormick

Data Miner, Trainer, Speaker, Author

Course details

Human-in-the-loop machine learning is all about continuous learning. And as a process, it’s becoming an increasingly common and critical component of emerging technologies. From healthcare analytics and computer vision to autonomous vehicles and natural language processing, human-in-the-loop machine learning is everywhere, but it’s still widely misunderstood. This course is intended to fill those gaps and quickly get you up to speed. Join instructor Keith McCormick as he explores the industry that has arisen to support this challenge, why it's so important, and how it relates to real-world tasks in data annotation. Discover some of the most common use cases within the larger human-in-the-loop ecosystem. Along the way, Keith shows you how to successfully implement and manage your own data annotation project, including basic skills required for quality control, sampling, active learning, and bias prevention.

Skills covered

Responsible AIMachine LearningArtificial Intelligence FoundationsFoundationsArtificial Intelligence (AI)

Concepts

0. Introduction

  • 01 - Meet the human-in-the-loop

1. Introducing Human-in-the-Loop

  • 02 - Human-in-the-loop is everywhere
  • 03 - What is supervised machine learning
  • 04 - Defining human-in-the-loop
  • 05 - The rise of human-in-the-loop

2. Common Use Cases

  • 06 - Healthcare use cases
  • 07 - Autonomous use cases
  • 08 - Natural language use cases
  • 09 - Reinforcement learning with human feedback (RLHF)

3. The Data Annotation Ecosystem

  • 10 - The world of data annotation vendors
  • 11 - Data annotation as a skill
  • 12 - The ethics of data annotation
  • 13 - Data annotation software vendors

4. Managing a Data Annotation Project

  • 14 - Quality control
  • 15 - Sampling
  • 16 - Considerations when managing your own project
  • 17 - Being data-centric

Conclusion

  • 18 - Next steps

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