Programme Parameters
Advanced Postgrad
3 Weeks (30 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Effective Data Labeling for AI Systems focuses on the input that quietly determines model quality: the labels. You learn to design annotation schemes and guidelines for text, image and other data, run the labelling workflow, and — crucially — control quality through inter-annotator agreement, review and error analysis. The course covers labelling tools, active learning to label smarter rather than more, and the practical management of human annotation teams and edge cases. You finish able to plan and run a labelling effort that produces training data your models can actually trust. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers data labelling for AI — annotation strategy, quality control, tooling and workflow design to produce reliable training data at scale.
📋 Course Objectives
1. Design annotation schemes and clear labelling guidelines.
2. Run labelling workflows for text, image and other data.
3. Measure and control quality with inter-annotator agreement.
4. Apply active learning to label efficiently.
5. Manage annotation tooling, teams and edge cases.
👥 Who Should Enroll?
• ML and data teams building training datasets
• Annotation and data-operations managers
• Data scientists improving model data quality
• Students specialising in applied ML
🚀 Key Learning Outcomes
• The ability to plan and run a data-labelling effort.
• Skills to produce reliable, high-quality training data.
• A quality-control framework for annotation.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Understanding the Role of Labeling in AI
Discover the importance of labeling in machine learning • Explore supervised, unsupervised, and semi-supervised labeling techniques • Learn about types of labels: classification, detection, segmentation, sequence
Annotation Task Design
Define labeling objectives and taxonomies • Ensure label consistency, granularity, and edge cases • Build clear annotation guidelines
Annotation Platforms and Tooling
Overview of labeling tools: Labelbox, CVAT, Prodigy, Doccano • Compare open source and commercial platforms • Annotate text, images, audio, and video with tool demos
Managing Human Annotation
Explore workforce models: in-house, crowdsourcing, managed services • Train annotators and ensure quality assurance • Implement inter-annotator agreement and review workflows
Scaling Labeling Pipelines
Manage dataset versioning and label management • Apply active learning and human-in-the-loop techniques • Use semi-automatic labeling and pre-labeling with AI
Strategy and Best Practices
Label for production-grade ML systems • Address ethical considerations: bias, privacy, fairness • Examine real-world case studies in computer vision and NLP
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Labelbox |
| Covered Tool / Platform | CVAT |
| Covered Tool / Platform | Prodigy |
| Covered Tool / Platform | Doccano |
Frequently Asked Questions
Scholar Feedback & Reviews
Based on 0 scholar submissions
Rating Breakdown
No verified reviews published yet. Be the first to share your academic experience.
Leave Scholar Feedback
Your rating will help prospective scholars. Ratings below 3 stars are routed privately to the faculty mentor for immediate response.
