8. Summary & Decision Guide
When starting an AI project, don't jump straight to the latest models (Transformers, GPT, etc.). The first question must be: "Do we have Labels?"
| Situation | Recommended Method | Representative Algorithms |
|---|---|---|
| We have plenty of labeled data | Supervised Learning | Random Forest, SVM, CNN, BERT |
| No labels, just want to see patterns | Unsupervised Learning | K-Means, PCA, Autoencoder |
| Lots of data, but broke (No $ for labeling) | Semi-Supervised Learning | Label Spreading, Self-training |
| Need to learn through trial & error | Reinforcement Learning | Q-Learning (AlphaGo), PPO |
The saying "Data is the new oil" is only half true. Crude oil (Raw Data) is everywhere. The real competitive advantage lies in refining it into gasoline (Labeled Data). Is your data crude oil or gasoline? If the former, try building a refinery (Semi-Supervised Learning) like I did.