You've done dozens of ML tutorials but can't build anything real. Math courses overflow with derivatives and backpropagation before you understand what problems they solve. You're stuck in tutorial hell, consuming content but never creating.

This roadmap breaks the cycle. Instead of starting with mathematical abstractions, we begin with concrete problems machine learning solves. Each post connects theory to real applications, building your intuition systematically from "what is ML?" to deploying production models that handle millions of users.

TLDR: Choose your learning path based on background and goals, follow decision-tree recommendations, and master ML from fundamentals to production deployment in a structured sequence.