Practical Data Labelling for Text and Image AI Projects Course
Discover practical data annotation for text and image tasks: sentiment labelling, named entity tagging, bounding boxes, and taxonomy design. Master tokenisation, preprocessing, and annotation schemes (BIO, BILOU, CoNLL, JSON); explore model-assisted labelling and active learning; QA, inter-annotator agreement, drift monitoring, and reproducible reporting. Deliverables are ready-to-submit with clear guidelines for real-world supervised learning.

from 4 to 360h flexible workload
certificate recognized by MEC
What will I learn?
Discover practical data annotation for text and image tasks: sentiment labelling, named entity tagging, bounding boxes, and taxonomy design. Master tokenisation, preprocessing, and annotation schemes (BIO, BILOU, CoNLL, JSON); explore model-assisted labelling and active learning; QA, inter-annotator agreement, drift monitoring, and reproducible reporting. Deliverables are ready-to-submit with clear guidelines for real-world supervised learning.
Elevify advantages
Develop skills
- Text sentiment labelling & NER: Master sentiment classes and named entity tagging.
- Annotation tool mastery: speed through templates, shortcuts, backups, exports.
- QA & adjudication: inter-annotator metrics, calibration, drift control.
- Data ethics & management: PII anonymisation, metadata, licenses, and reports.
Suggested summary
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