P2.2.2 ML Decision Framework | ML Foundations - Python to GenAI

Most beginners make the same mistake — they jump straight to an algorithm without asking the right questions first. In this session (P2.2.2 — ML Decision Framework), you'll learn the exact thinking process professionals use to pick the right algorithm before writing a single line of code. → The 3 questions you must ask for every ML problem → Supervised vs Unsupervised — how to decide instantly → Regression vs Classification — what's the difference and when to use each → Which algorithms we cover: Linear Regression, Naive Bayes, K-Means → The standard ML workflow — Split → Train → Evaluate → Inference → Overfitting vs Underfitting explained with real numbers → Why the TASK drives the algorithm — not the other way around No random algorithm picking. No confusion. Just a clean repeatable decision framework you'll use for every ML problem going forward. 📂 Part of the GenAI Foundation Course 📁 GitHub https://github.com/MaighaInc/pycore/tree/main/Course-GenAIFoundation/P2_LegacyToGenAI/P2.2_ML%20Foundations 💬 Discord https://discord.com/invite/V35dKcApS6 ────────────────────────────── 🔔 Subscribe so you don't miss the next session 👍 Like if the framework clicked for you 💬 Drop your questions below — I read every one ──────────────────────────────