Machine Learning Basics
How machines learn from data — the paradigms, the workflow, and why generalization is the whole game.
beginner · 45 minutes
A ground-up, intuition-first tour of how machine learning actually works. You will learn the three learning paradigms, how models are *fit* to data, why the train/validation/test split is sacred, and how to tell a genuinely useful model from one that only *looks* good. No heavy math required, just clear thinking.
What you'll learn
- What Machine Learning Is (and Isn't)
- How a Model Learns: Parameters, Loss, and Optimization
- Generalization and the Train/Validation/Test Split
- Overfitting, Underfitting, and the Bias-Variance Tradeoff
- Evaluation: Why Accuracy Lies
- The End-to-End ML Workflow and Classic Pitfalls