AI FOUNDATIONS
Python for AI
Learn Python from fundamentals to advanced concepts, with a practical focus on how each concept is used when building real AI applications.
Python Learning Path
Follow the modules in order. Each module builds on the concepts introduced before it.
Python Foundations
Learn the fundamental Python concepts needed to build AI applications, from variables and data types to strings, operators and input/output.
Python Collections
Learn how Python stores and manages collections of data using lists, tuples, sets and dictionaries, with practical examples from AI applications.
Python Control Flow
Learn how Python makes decisions and repeats work using if/elif/else, for loops, while loops, and loop control statements, with AI examples like filtering documents and retry logic.
Python Functions
Learn how to write reusable Python logic with functions, parameters, default and keyword arguments, *args/**kwargs, scope, lambdas, and comprehensions, with practical AI preprocessing examples.
Object-Oriented Python
Learn classes, objects, inheritance, polymorphism, composition, and dunder methods in Python, and how they're used to build model wrappers, tools, and agents in AI applications.
Errors and Exception Handling
Learn how to handle failures gracefully in Python using try/except/else/finally, custom exceptions, and retry logic, with realistic examples of handling AI API failures and timeouts.
Files and Data
Learn how to read and write text files, CSV data, and JSON in Python, and how these map directly to the request and response format used by every AI API.
Modules, Packages and Environments
Learn how to organize Python code into modules and packages, manage dependencies with pip and virtual environments, and securely handle AI API keys using .env files.
Python for Data and AI (NumPy and Pandas)
Learn the essential NumPy and Pandas concepts needed for AI development, including arrays, shapes, vector operations, cosine similarity, DataFrames, and filtering evaluation datasets.
Advanced Python
Learn advanced Python patterns used inside real AI frameworks, including generators, decorators, closures, context managers, type hints, dataclasses, enums, and pattern matching.
Working with APIs
Learn HTTP fundamentals, request and response handling, status codes, timeouts, and retries in Python, and how to call and parse real LLM APIs.
Logging and Production Python
Learn how to replace print statements with proper logging in Python, using logging levels, formatting, and file logging, and understand what should never appear in AI application logs.
Async Python
Learn synchronous versus asynchronous programming in Python using async/await and asyncio, and how to run concurrent LLM API calls and agent tool calls efficiently.
Python for Modern AI Development
Learn how embeddings, RAG, vector databases, agents, tool calling, structured outputs, and Pydantic validation are built from core Python concepts, and where LangChain, LangGraph, and MCP fit in.
Unit Testing and Mock Testing
Learn how to write automated tests for AI code using pytest, fixtures, and parametrized tests, and how to mock LLM API calls with unittest.mock so tests run fast, free, and reliably.
Data Cleaning for AI and Your First AI Data Pipeline
Learn how to clean, normalize, deduplicate, and chunk raw text for AI applications, and build a complete end-to-end data pipeline from raw documents to a queryable vector store.
WHY PYTHON FOR AI?
Learn Python through the lens of AI
Instead of learning Python as isolated syntax, each concept is connected to how it appears in AI applications, APIs, RAG pipelines, agents, data processing and production systems.