Break into AI: Data Annotation Essentials
15mBeginner2025-12-17
Authors
Christopher Cameron
Course details
Explore the essentials of data annotation with AI strategist Christopher Cameron. Get an introduction to identifying simple objects and progress to building rubric data for evaluating AI model responses. Learn how to apply core annotation skills, including creating ideal answers and setting evaluation criteria. Discover the importance of rubric data for training large language models (LLMs) and analyze best practices for building effective criteria. Find out how professionals from all backgrounds can contribute to AI training. This course is ideal for LinkedIn members looking to enhance their skill set in AI model training. With practical exercises and a focus on real-world applications, this course offers a unique opportunity to help shape the future of AI.
Concepts
Introduction
- Annotation quick start - Identify simple objects
Annotation Basics
- Build evaluation criteria
- Challenge - Evaluate model responses
- Solution - Evaluate model responses
Building Rubric Data
- What is rubric data
- From simple to complex - Building rubric data
- Challenge - Build rubric data for an email
Conclusion
- Apply your annotation skills
Related courses
- Introduction to Career Skills in Data Analytics
- Complete Guide to Generative AI for Data Analysis and Data Science
- AI in Risk Management and Fraud Detection
- AI Product Foundations: Planning Strategies for Data Scientists
- Data Analytics: Dashboards vs. Data Stories
- AI Agents in Your Browser: Boosting Productivity with Gemini in Chrome
- GenAI and Predictive AI Architecture Foundations
- AI in RAN (Radio Access Network): Transforming Mobile Networks
Related learn paths
- Masterpath in Analytics Leadership for Executives
- AI Essentials for Business Analysis
- Prepare for the CompTIA Security+ (SY0-601) Certification Exam
- Essential New Skills in Project Management
- Become a Business Intelligence Specialist
- Build Essential Data Skills
- Get Ahead in Business Analytics and Analysis
- Moving from Data Scientist to Data Analyst