ML Basics for Data People

What machine learning actually is from a data perspective - learning patterns from history instead of hand-writing rules, the workflow from features to evaluation, and why ML lives or dies on your data.

1. What ML Actually Is (for Data People) Machine learning learns patterns from historical data to predict on new data, instead of you hand-writing rules - supervised learning predicts from labeled examples, unsupervised finds structure with no labels.
2. The Workflow How a supervised ML project actually flows: features are the input columns the model learns from, you split into train and test to measure on unseen data, you train, then you evaluate - and accuracy alone can hide a useless model.
3. Where Data People Fit The unglamorous truth: ML lives or dies on data - clean inputs, good features, leak-free splits, reliable pipelines. The model is the easy part, and that's exactly where a data person is most valuable.