Python Essentials for AI Engineer
For beginners preparing for AI-engineer interviews who need to answer core Python questions with confidence. You will be able to explain data types, functions, iterators, OOP, files, modules and exceptions in a short spoken answer, and back it up with runnable examples from real data and LLM work.
Course Content
Prepares you to explain what Python is, why AI runs on it, and how its core types — numbers, strings, lists, tuples, dicts and sets — behave, including the traps interviewers like to test.
Prepares you to explain how Python functions take arguments, return results and resolve names, and to spot the classic traps — mutable defaults, `global` state and misused lambdas — in data and LLM code.
Prepares you to answer questions on everyday list and dict methods, `enumerate`, comprehensions and the iterator protocol — including which operations mutate in place, what they cost, and how to stream data too large for memory.
Prepares you to explain classes, `__init__`, `self`, the four pillars of OOP and dunder methods in Python terms, using the patterns you meet in PyTorch, scikit-learn and LLM client code.
Prepares you to answer questions on opening, reading and writing text, CSV and JSON files safely, and on how Python modules, imports and the `__name__ == "__main__"` guard work in a real project.
Prepares you to explain how Python exceptions work and to show you can handle failures the way production AI code must — catching the right errors from files and LLM APIs, cleaning up reliably, and designing your own exception types.