A practical guide to choosing between Supervised, Unsupervised, and Semi-Supervised Learning when you don't have labeled data.
Solving server waste at dawn and crashes at lunch. Understanding Auto Scaling vs Serverless through 'Taxi Dispatch' and 'Pizza Delivery' analogies. Plus, cost-saving tips using Spot Instances.
ChatGPT answers questions. AI Agents plan, use tools, and complete tasks autonomously. Understanding this difference changes how you build with AI.
I actually used all three AI coding tools for real projects. Here's an honest comparison of Copilot, Claude Code, and Cursor.
Converting words and sentences into numeric vectors lets you do math on meaning. From cosine similarity and ANN algorithms to the OpenAI embeddings API, here's everything you need to know.
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.
