Machine Learning: Data Analysis 2017

Responsible AI is the practice of building and running systems whose decisions you could defend to the person on the receiving end of them, covering fairness, transparency, privacy and accountability.

Machine learning is the branch of computing in which a program works out its rule from examples instead of being handed that rule by a programmer, and is judged on how well the rule holds on cases it was never shown.

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.

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.