A complete Python course taking you from beginner to advanced. You'll go from "Hello, World!" through variables, loops, collections, functions, and OOP, then on to generators, asyncio, testing with pytest and dataclasses, advanced typing (Generic, Protocol, TypedDict), design patterns, profiling (cProfile, memory_profiler, dis), multiprocessing and the GIL, and publishing packages to PyPI. Each lesson ends with 4 review questions.
You'll learn about your first program, variables, data types, and how to interact with the user.
Conditional statements and loops — the building blocks of program logic.
Lists, dictionaries, tuples, and sets — the data structures you use to store your data.
Functions let you reuse code across your programs. Modules organize code into files and packages.
Classes and objects, exception handling, and file operations — an introduction to intermediate Python.
Lambdas, closures, decorators, and type hints — the tools that separate a beginner from an intermediate Python developer.
Classes, instances, inheritance, polymorphism — the foundation of large Python programs.
`try`/`except`/`else`/`finally`, `raise`, custom exceptions, context managers — writing programs that don't crash and burn.
Reading and writing text files, JSON, and CSV, plus using the modern `pathlib` module instead of `os.path`.
`datetime`, `collections`, `itertools`, `functools` — stdlib tools that save you from writing everything from scratch.
`yield`, generator expressions, the iterator protocol — lazy evaluation and infinite sequences without the memory overhead.
async/await, coroutines, gather, asyncio.run — concurrent I/O operations without threads.
Test functions, assertions, fixtures, parametrize, mocking — pytest as the standard testing tool in the Python ecosystem.
`@dataclass` and `Enum` — modern ways to structure data without boilerplate.
venv, pip, requirements.txt, mypy, ruff, black, and project structure — the modern Python ecosystem.
Generics, Protocol, TypedDict, Literal — advanced Python typing for mature codebases with mypy/pyright.
Classic GoF patterns implemented in Python — often simpler than their Java/C++ equivalents, thanks to the language's dynamic capabilities.
Measuring and optimizing code — timeit, cProfile, memory_profiler, dis. Measure first, then optimize.
Threading, multiprocessing, concurrent.futures, and the GIL. Learn when to use each approach — async vs threads vs processes.
Learn how to create and distribute Python packages using `pyproject.toml`, `build`, `twine`, and `wheel`. Find out how to publish your library to PyPI.
`ndarray`, broadcasting, indexing, ufuncs — NumPy is the foundation of Python's entire AI/ML stack. C-level performance with vectorized operations.
DataFrame, Series, groupby, merge, time series. Pandas is the standard tool for ETL and data exploration in Python.
Figure/Axes, subplots, customization, and the Seaborn high-level API. Visualization is key to exploring data and communicating model results.
Learn scikit-learn's fit/predict API, Pipeline, train/test split, metrics, and cross-validation. sklearn is the de facto standard for classical ML in Python.
Tensor API, autograd, device management (CPU/CUDA/MPS), nn.Module, and the training loop. PyTorch is the standard framework for deep learning research and is increasingly used in production.
