Week 3: Why I'm Learning 'Boring' ML Before Building with LLMs

Week 3 done. This week I learned shallow algorithms - Linear Regression, Logistic Regression, DBSCAN, PCA. Not LLMs. Not ChatGPT integrations. Not the AI applications everyone's building. Basic machine learning algorithms from decades ago. And I kept asking myself: why am I doing this? The Question I Keep Getting "You're learning AI, right? When are you building something with GPT or Claude?" Fair question. I could skip straight to LLM applications. Plenty of people do. But here's what I realized this week: I want to understand what's actually happening, not just call APIs. Why Shallow Algorithms First 1. They're what's actually running in production Most companies aren't running massive neural networks. They're running: Logistic regression for fraud detection Linear regression for demand forecasting Clustering for customer segmentation PCA for feature reduction The "boring" algorithms power real systems. 2. They teach you how ML actually works When I call an LLM API: response = openai