Machine Learning Mastery (Integrated Theory+Practical HW)

Prompt engineering is the practice of composing what you send a generative model, the instruction, the supporting material and the worked examples, so that it returns something you can actually use.

Retrieval-augmented generation is a pattern that searches a body of documents when a question arrives and places the passages it finds into the model's prompt, so the answer is drawn from those sources rather than from training alone.

Natural language processing is the area of AI that makes human language readable and writable by machine: sorting text into categories, pulling structured facts out of it, translating it, generating it and measuring whether any of that came out right.

SQL is the declarative language for asking questions of relational databases: you state which rows and columns you want and how tables relate, and the database engine works out how to retrieve them.

Data analysis is the practice of interrogating data to answer a specific question, and of establishing how much weight the answer can bear.

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.