Stage 1 · Lesson 1 of 6
Labelled data and why models need it
Most machine learning still learns from examples with answers attached. That is : show a model thousands of photos marked "cat" or "dog", or emails marked "spam" or "not spam", and it learns the pattern. The answers are called labels, and the people who add them are .
A model can only be as good as its labels. If one annotator draws loose boxes and another draws tight ones, the model learns confusion. That is why companies pay for human labelling, write long rulebooks and check the work.
Self-driving cars, medical image tools, speech recognition in Urdu, Bangla or Swahili, and document readers all depend on it.
Common trapthinking labelling is mindless clicking. It is careful, rule-following work, and quality is what gets you paid and kept on projects.
A question to check yourself, an AI practice session and 1 cheat sheet entry come with the full roadmap. Unlock
Resources for this stage
Start with· follow one of these
- CoursePaidGrokking Modern AI Fundamentals
Design Gurus · Beginner · $72 one-time
A structured course on how modern AI models are built and trained, for the full picture behind your tasks
- VideoFreeDeep Dive into Large Language Models like ChatGPT
Andrej Karpathy · Intermediate
Free: Karpathy walks through pre-training, fine-tuning and RLHF, including the role of human labellers