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Practical Data Labelling for Text and Image AI Projects Course

Practical Data Labelling for Text and Image AI Projects Course
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

Before starting, you can change the chapters and workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.
Workload: between 4 and 360 hours

What our students say

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Very great course. Lots of rich information.
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