Almost every AI tool you will use, from data libraries to model frameworks to LLM SDKs, is driven from Python. You don't need to be a Python expert to start, but you do need to read and write it comfortably. This module covers the core language: syntax and data structures, functions and modules, and virtual environments for keeping each project's packages separate.
It then moves to the habits that make AI code easier to maintain: type hints, object-oriented programming with classes and inheritance, and Jupyter notebooks for experimenting interactively. The last lessons cover asyncio, which you'll rely on when making many model calls at once, and working with APIs.
Watch the lessons in order, then continue on to Math & Statistics.
Installing Python, then numbers, strings, lists, tuples, sets, and dictionaries, plus control flow, modules, exceptions, classes, and virtual environments.
A deeper pass through the language with a focus on types: variables, functions, args and kwargs, enums, decorators, error handling, and modules and packages.
Classes and objects, attributes and methods, properties, encapsulation and abstraction, inheritance, and polymorphism, the patterns behind most AI frameworks.
A tour of the Jupyter project, focusing on JupyterLab and Jupyter Notebooks, the interactive environment most AI experiments start in.
Synchronous versus asynchronous code, the event loop, coroutines with async and await, tasks, gather, TaskGroup, and locks and semaphores for throttling concurrent work.
What an API is and how the web works, exploring APIs with Postman, using helper libraries in Python, and building a small project that calls an API with Flask.