📚 Syllabus & Course Curriculum
Nanotechnology & Materials Science
Module-by-module breakdown of Effective Data Labeling for AI Systems, from foundations to a certified capstone project.
Outline
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
Outline
Define labeling objectives and taxonomies • Ensure label consistency, granularity, and edge cases • Build clear annotation guidelines
Outline
Overview of labeling tools: Labelbox, CVAT, Prodigy, Doccano • Compare open source and commercial platforms • Annotate text, images, audio, and video with tool demos
Outline
Explore workforce models: in-house, crowdsourcing, managed services • Train annotators and ensure quality assurance • Implement inter-annotator agreement and review workflows
Outline
Manage dataset versioning and label management • Apply active learning and human-in-the-loop techniques • Use semi-automatic labeling and pre-labeling with AI
Outline
Label for production-grade ML systems • Address ethical considerations: bias, privacy, fairness • Examine real-world case studies in computer vision and NLP
Earn government-registered certification in Effective Data Labeling for AI Systems
e-Certificate and e-Marksheet issued on successful completion.
