Step 1: Foundations
Start with programming and logic: functions, data structures, and data handling. Then build basic statistics: mean/median, variance, probability intuition.
Step 2: Data handling
Data is everything in AI/ML. Learn cleaning, basic feature thinking, and dataset understanding. In projects, extract insights from CSV/JSON data.
Step 3: Model concepts
Understand regression, classification, overfitting, train/test, and metrics (accuracy/precision/recall). Then build small model-based projects.
Next step
If you're starting out, build strong programming fundamentals first—then follow the AI/ML roadmap with confidence.