Every AI method is designed around a kind of data. Tabular data is the home of decision trees and boosted ensembles like XGBoost and LightGBM. Text needs models that handle sequence and context, which is where recurrent networks gave way to the transformer and its attention mechanism. Images are grids of pixels that convolutional neural networks learn to see. Networks are all about relationships, and graph neural networks pass messages between connected nodes.

In this video, you'll get a clear, non-technical introduction to each method, plus the concepts of loss, training, evaluation, and hyperparameters, so you can follow how a problem becomes a working model.