Generative AI changes how we code. This preface introduces Code Literacy—why reading code is now as important as writing it—and how to effectively collaborate with AI tools.
Understand how computers store information using bits and bytes. Learn about binary representation, memory management, and why understanding data types is crucial for AI efficiency.
Master Python's numeric types. Learn the difference between Integers and Floats, handle scientific notation for AI parameters, and avoid common floating-point errors.
Text is the fuel of Large Language Models. Learn to manipulate strings, format prompts effectively, and handle Unicode characters and Emojis correctly.
Control the logic of your software. Understand Truthiness, short-circuit evaluation, and how Boolean logic acts as the safety guardrail for AI decision-making.
Master Python's essential sequence types. Learn when to use mutable Lists versus immutable Tuples, and see how they form the basis of AI Tensors and Embeddings.
Unlock the power of Hash Tables. Learn how Dictionaries and Sets provide ultra-fast O(1) data retrieval and how they structure JSON data for API communication.
Direct the flow of your programs. Master if statements, for loops, and while
loops to automate repetitive tasks and process data streams.
Write cleaner, more "Pythonic" code. Use List, Dictionary, and Set comprehensions to transform data concisely and learn Generator Expressions for memory efficiency.
Build modular, reusable code. Learn to define functions with Type Hints and Docstrings—essential skills for creating tools that AI agents can utilize.
Build resilient software. Learn to catch errors using try-except blocks, implement retry
logic for API calls, and use Assertions for defensive programming.
Persist your data. Learn to read and write text and binary files, handle character encodings safely, and manage JSON data for AI datasets.
Organize your code effectively. Learn to manage dependencies with pip and Virtual
Environments, and structure your projects using Modules and Packages.
Compare the major coding styles: Procedural, Object-Oriented, and Functional. Understand how AI workflows combine these paradigms for model definition and training.
Model the real world with code. Master Classes, Inheritance, and Magic Methods, and see how Deep Learning libraries use OOP to define Neural Networks.
Write predictable, bug-free code. Explore Pure Functions, Immutability, and higher-order functions like
map and reduce for efficient data pipelines.
Write code that humans can read. Master PEP 8 standards, effective naming, and Type Hinting to create maintainable software that serves as a better prompt for AI.
Not all code is created equal. Learn to measure algorithm efficiency using Big-O notation and understand the difference between O(1) and O(N²) performance.
Go beyond the basics. Explore Linked Lists, Stacks, Queues, Trees, and Graphs, and understand how they power everything from undo buttons to Neural Networks.
Ensure your code works. Master testing with pytest, learn to Mock API calls, and understand
the unique challenges of evaluating non-deterministic AI outputs.