Build the professional Python foundation every AI engineer needs. Over four weeks, master modern type hinting, data manipulation with Pandas, structured LLM outputs with Pydantic, and async programming — culminating in a fully async data pipeline.
No rating yet
4 weeks
What You'll Learn
Configure professional Python environments with linting and virtual environments
Apply advanced type hinting including Union types, Optional, and variadic generics
Ingest and transform nested JSON and REST API data using Pandas
Define Pydantic schemas for Tool Output, Native Output, and Prompted Output modes
Write concurrent async pipelines using asyncio.gather and Semaphore
Course Content
W1
Week 1: Modern Python & Type Hinting
Write production-grade Python from day one.
1
Professional Environment Setup
Configuring linting tools, formatters, and virtual environments to enforce code quality from the first line.
2
Python 3.12 Core Fundamentals
Core language mechanics and memory management patterns that matter for long-running AI workloads.
3
Advanced Type Hinting
Union types, Optional, and Literal annotations that make AI function signatures self-documenting and safer.
4
Variadic Generics & @override
Using variadic generics and the @override decorator to write extensible, type-safe class hierarchies.
Weekly Win
Static Analysis for CI/CD
Integrate a static analysis tool into a CI/CD pipeline so every commit is automatically checked for type errors before merge.
W2
Week 2: Data Manipulation for AI
Turn raw API responses into clean, model-ready DataFrames.
1
Data Ingestion for REST APIs
Mechanics of consuming paginated REST APIs and handling deeply nested JSON responses reliably.
2
Pandas DataFrames Basics
Loading, inspecting, and handling missing values in DataFrames to prepare data for downstream AI tasks.
3
Complex JSON Parsing
Flattening deeply nested JSON structures into tabular form using pd.json_normalize with custom record paths.
4
Data Aggregation & GroupBy
Summarising large datasets with GroupBy operations, pivot tables, and multi-level aggregations.
Weekly Win
AI-Assisted Data Workflows
Use GitHub Copilot to accelerate a data transformation task, measuring the time saved against a manual implementation.
W3
Week 3: Structured Outputs & Pydantic
Force LLMs to return data your code can actually use.
1
Pydantic Models & Dataclasses
Defining strict schemas using Pydantic BaseModel and Python dataclasses to validate every field an LLM returns.
2
Output Modes
Implementing Tool Output, Native Output, and Prompted Output modes — and knowing when each is appropriate.
3
Dynamic Validation Contexts
Injecting runtime context into Pydantic validators so schema rules can adapt to changing business logic.
4
Output Validators & Retries
Attaching field validators to catch malformed LLM responses and triggering automatic model retries on failure.
Weekly Win
Streaming LLM Responses
Build an advanced parser that streams a live LLM response and progressively validates each chunk against a Pydantic schema.
W4
Week 4: Async Programming & Capstone
Run parallel AI calls without blocking your entire application.
1
The Event Loop & async/await
How Python's event loop works and the correct mental model for writing non-blocking async functions.
2
Concurrent Execution with asyncio.gather
Firing multiple LLM or API calls simultaneously and collecting results without sequential bottlenecks.
3
Resource Management & Connection Pooling
Using async context managers and connection pools to avoid resource exhaustion in high-throughput pipelines.
4
Concurrency Limits with asyncio.Semaphore
Capping parallel requests with Semaphore to respect API rate limits without sacrificing throughput.
Weekly Win
Capstone: Async Pipeline
Build an async "Messy-to-Structured" pipeline that ingests unstructured data, calls an LLM concurrently, and validates every output through Pydantic.