Data Labeling Course

Lesson 1 • Key Stakeholders in a Labeling Project

Identifies the roles involved in a labeling pipeline from client to annotator. Clarifies responsibilities and communication flows.

Lesson 2 • Major Data Labeling Task Types

Surveys the primary categories of labeling tasks across modalities. Gives learners a map of the full scope of labeling work.

Lesson 3 • AI and Machine Learning Context

Explains how labeled datasets train, validate, and test ML models. Connects labeling quality directly to model performance outcomes.

Lesson 4 • Labeling Workflow Overview

Traces the end-to-end lifecycle of a labeling project from intake to delivery. Prepares learners to understand each stage covered in later chapters.

Lesson 1 • Designing Annotation Tasks

Covers how task structure affects annotator speed, accuracy, and fatigue. Learners design tasks that balance throughput with quality.

Lesson 2 • Maintaining and Updating Guidelines

Addresses how guidelines evolve as edge cases emerge during production. Establishes processes to keep all annotators aligned with the latest version.

Lesson 3 • Writing Clear Label Definitions

Teaches techniques for defining labels precisely to eliminate annotator guesswork. Directly reduces inter-annotator disagreement on ambiguous cases.

Lesson 4 • Purpose and Structure of Guidelines

Explains why well-written guidelines are the single biggest driver of label consistency. Covers the essential components every guideline document must include.

Lesson 1 • Polygon and Polyline Annotation

Teaches precise polygon drawing for irregularly shaped objects and polylines for lanes or paths. Builds on bounding box skills with higher-precision techniques.

Lesson 2 • Video Annotation and Object Tracking

Extends image annotation skills to temporal sequences with object tracking. Covers interpolation, track IDs, and handling object entry and exit.

Lesson 3 • Bounding Box Annotation

Covers the rules and best practices for drawing tight, accurate bounding boxes. Establishes the baseline visual annotation skill used in object detection tasks.

Lesson 4 • Semantic and Instance Segmentation

Distinguishes semantic from instance segmentation and explains when each is required. Learners practice pixel-level labeling for dense annotation tasks.

Lesson 1 • Instruction Following and RLHF Labeling

Introduces preference ranking and response evaluation tasks used in reinforcement learning from human feedback. Prepares annotators for generative AI alignment work.

Lesson 2 • Text Span and Highlight Tasks

Covers tasks where annotators highlight evidence spans, rationales, or toxic content. Connects span selection precision to downstream model explainability.

Lesson 3 • Relation and Coreference Annotation

Introduces linking tasks that connect entities and resolve pronoun references. Builds on NER skills to capture relational structure in text.

Lesson 4 • Sentiment and Intent Classification

Teaches document-level and sentence-level sentiment and intent labeling. Addresses subjectivity challenges that require clear guideline anchoring.

Lesson 1 • Audio Event and Sound Classification

Introduces labeling of non-speech audio events such as environmental sounds and music. Covers taxonomy design and temporal boundary marking for sound events.

Lesson 2 • Speaker Diarization and Segmentation

Teaches how to identify and label individual speakers across an audio recording. Builds on transcription skills by adding temporal speaker attribution.

Lesson 3 • Multimodal Annotation Alignment

Addresses tasks that require synchronizing labels across text, image, and audio modalities. Prepares learners for video captioning and audio-visual correspondence tasks.

Lesson 1 • Platform Security and Data Handling

Addresses data privacy, access controls, and secure handling of sensitive annotation data. Prepares learners to operate within organizational and regulatory data requirements.

Lesson 2 • Data Import and Export Pipelines

Covers how raw data enters annotation platforms and how completed labels are exported. Addresses common format standards and integration with ML training pipelines.

Lesson 3 • Annotation Efficiency Features

Introduces platform features that accelerate annotation without sacrificing quality. Learners apply pre-labeling, shortcuts, and smart tools in practice.

Lesson 4 • Project Setup and Configuration

Walks through the steps to configure a labeling project including label schema, task settings, and annotator assignment. Builds operational readiness for production labeling.

Lesson 1 • Review and Adjudication Workflows

Teaches structured review processes where senior annotators resolve disagreements. Connects review outcomes to guideline updates and annotator coaching.

Lesson 2 • QA Metrics and Reporting

Covers the dashboards and reports used to communicate quality status to stakeholders. Establishes the KPIs that define project health and delivery readiness.

Lesson 3 • Gold Standard and Honeypot Tasks

Covers the use of pre-labeled gold items embedded in annotator queues to measure accuracy. Explains how honeypots detect low-effort or fraudulent annotation.

Lesson 4 • Inter-Annotator Agreement Metrics

Introduces statistical measures of agreement between annotators as the primary quality signal. Learners calculate and interpret Cohen's kappa, Fleiss' kappa, and F1-based agreement.

Lesson 1 • Stakeholder Communication and Reporting

Covers how to structure progress updates, escalate risks, and present quality reports to clients. Builds the communication skills needed to maintain client trust.

Lesson 2 • Annotator Recruitment and Onboarding

Addresses how to source, screen, and onboard annotators for a specific task type. Ensures annotators are qualified and aligned with guidelines before production begins.

Lesson 3 • Scoping and Estimating Labeling Projects

Covers how to translate a client's ML data need into a defined project scope with effort estimates. Prevents scope creep and sets realistic delivery expectations.

Lesson 4 • Production Monitoring and Throughput

Teaches how to track daily output, identify bottlenecks, and maintain velocity targets. Connects throughput monitoring to on-time delivery management.