Data Annotation for AI/ML Bootcamp

This course contains the use of artificial intelligence.

Every AI model you've heard of was trained on data that a human labeled first. Bounding boxes, transcripts, entity tags, segmentation masks. Somebody drew them, and that somebody gets paid. This bootcamp teaches you to be that person, and to be good enough at it that platforms and teams want you back.

You will not just watch. You'll use an annotation tool, set up your own projects, and label real data.

What you'll actually do

• Set up a real annotation environment. Install and configure CVAT, the open-source tool used across the industry, and get comfortable navigating its interface the way a working annotator does, not the way a tutorial skims it.

• Build projects and tasks from scratch. Create your first project, define label sets, structure tasks, and understand the decisions an annotation lead makes before a single box gets drawn.

• Learn every image annotation shape and when to use it. Bounding boxes for object detection. Polygons for precise shapes. Polylines for lanes and boundaries. Points for keypoints and landmarks. Ellipses for circular objects. Cuboids for 3D. Masks for pixel-level work. You'll practice each one and learn the judgment call for picking the right tool for the job, which is what separates a fast annotator from an accurate one.

• Tag objects and frames with attributes. Add the metadata layer that turns a shape into usable training data: occlusion, truncation, class attributes, frame-level tags.

• Track objects through video. Move from static images into temporal annotation. Propagate shapes across frames, handle the outside property on the last frame, and use the full track toolkit: merge, group, split, join, and slice. This is the module most annotators fumble, and it's where the better-paying work lives.

• Annotate text across four task types. Text classification, named entity recognition, sequence labeling with relation tags, and annotation specifically for LLM training. You'll see how the same passage gets labeled differently depending on what the model is meant to learn.

• Work with real guidelines and real edge cases. Read guidelines critically, apply them consistently, and handle the ambiguous cases that guidelines never quite cover. Edge cases are where quality scores are won and lost.

• Transcribe and annotate audio. Verbatim versus clean transcription and when each is required. Transcription rules and notation. Speaker diarization. Sound event and emotion tagging. Then three hands-on practice activities: repairing a broken transcript, converting between transcription styles, and deciding what to tag and what to leave alone.

• Measure your own quality. Calculate inter-annotator agreement by hand so you understand what the number actually means. Learn how platforms score you through gold tasks and QA sampling, and what to do when your score drops.

• See inside a real annotation project. Walk through how a project is scoped, staffed, and reviewed, and how the tool landscape maps to different job types so you know which platform fits which kind of work.

• Confront bias before it ships. Spot bias in datasets and in your own labeling behavior, and apply mitigation strategies that hold up under review.

• Test yourself. Finish with a practice test that checks whether the workflow, the vocabulary, and the judgment actually stuck.

This course contains a promotion.