Working with Text and Data in Python: Regex, JSON, and CSV
Lesson 11: turn messy text into structured data: a practical regex core, JSON round trips, CSV with DictReader, and a complete validate-and-transform pipeline from CSV to clean JSON.
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Lesson 11: turn messy text into structured data: a practical regex core, JSON round trips, CSV with DictReader, and a complete validate-and-transform pipeline from CSV to clean JSON.
Lesson 5: lists, tuples, sets, and dictionaries, how to choose between them, and the comprehensions, enumerate, and zip idioms that make Python feel like Python, with a word-counter project.
Lesson 6: object-oriented Python without the fear: classes, __init__ and self, dunder methods, inheritance versus composition, and modern dataclasses, with an inventory project in the playground.
Lesson 7: write Python that survives bad input: tracebacks, try/except done right, raising custom exceptions, and safe file reading and writing with the with statement, practiced on messy data.
Lesson 8: imports demystified, your own modules and packages, pip and virtual environments, then a working tour of math, random, datetime, Counter, defaultdict, and pathlib, ending in a file organizer project.
Lesson 9: the machinery behind every for loop, then generators with yield, lazy generator expressions, infinite sequences, and constant-memory data pipelines, finished with a log analyzer project.
Lesson 10: make Python explain itself with type hints: annotations, typed collections, unions with None, type aliases, and a type checker that finds bugs before you run anything.
Lesson 14: train your first real machine learning models with scikit-learn: features and labels, train/test splits, fit/predict/score, a decision tree you can read, k-nearest-neighbors, and overfitting, live in the browser.
The finale: LLMs explained with the concepts you already own, tokens and prompts, your first Claude API call from Python, a toy RAG engine in the browser, and the full map into the production LLM and MCP series.
Lesson 15: deep learning without hand-waving: what neurons compute, loss and gradient descent, backpropagation built by hand in NumPy in the browser, and a complete Keras Fashion MNIST classifier with dropout and transfer learning.