Effective Data Labeling for AI Systems

Aim

Program Objectives

Program Structure

Week 1: Foundations of Data Labeling
Module 1: Understanding the Role of Labeling in AI

• Chapter 1.1: Why Labeling Matters in Machine Learning

• Chapter 1.2: Supervised vs. Unsupervised vs. Semi-Supervised Labeling

• Chapter 1.3: Types of Labels: Classification, Detection, Segmentation, Sequence

Module 2: Annotation Task Design

• Chapter 2.1: Defining Labeling Objectives and Taxonomies

• Chapter 2.2: Label Consistency, Granularity, and Edge Cases

• Chapter 2.3: Building Clear Annotation Guidelines

Week 2: Tools, Techniques, and Quality Control
Module 3: Annotation Platforms and Tooling

• Chapter 3.1: Overview of Labeling Tools (Labelbox, CVAT, Prodigy, Doccano)

• Chapter 3.2: Open Source vs. Commercial Platforms

• Chapter 3.3: Annotation Tool Demos (Text, Image, Audio, Video)

Module 4: Managing Human Annotation

• Chapter 4.1: Workforce Models: In-house, Crowdsourcing, Managed Services

• Chapter 4.2: Annotator Training and Quality Assurance

• Chapter 4.3: Inter-Annotator Agreement and Review Workflows

Week 3: Scaling, Automation, and Strategy
Module 5: Scaling Labeling Pipelines

• Chapter 5.1: Dataset Versioning and Label Management

• Chapter 5.2: Active Learning and Human-in-the-Loop

• Chapter 5.3: Semi-Automatic Labeling and Pre-labeling with AI

Module 6: Strategy and Best Practices

• Chapter 6.1: Labeling for Production-Grade ML Systems

• Chapter 6.2: Ethical Considerations in Labeling (Bias, Privacy, Fairness)

• Chapter 6.3: Real-World Case Studies in Computer Vision and NLP

Who Should Enrol?

Program Outcomes

Fee Structure

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What You’ll Gain