Data Science and Machine Learning Masterclass with R

A neural network is a stack of layers of very simple arithmetic units whose connection strengths are adjusted by training until the whole arrangement turns the inputs you have into the outputs you want.

Deep learning is machine learning built on neural networks of many stacked layers, which work out for themselves which properties of the raw data matter instead of being handed a list by a person.

Generative AI is the market's name for models whose output is a piece of content, such as text, images, audio, video or code, as opposed to the much larger body of models that sort, score or forecast things that already exist.

Data wrangling is the work of repairing and reshaping real records, and of deciding what their gaps and inconsistencies mean, until a table can be trusted with the question being asked of it.

Data visualisation is the practice of encoding numbers as position, length and colour so that a comparison a reader would otherwise have to calculate becomes something they can simply see.

Data engineering is the discipline of moving records from the systems that produce them into a store where they can be queried, and of keeping that supply correct while the sources underneath keep changing.