Overview

Most Python courses teach the language. This one teaches you to build real AI applications with it: systems that call language models, retrieve from large document collections, run reliably under load, and stay affordable in production. The course follows one project from start to finish. You begin with a clean, reproducible project setup and a simple script that processes documents. Over twelve modules it grows into a deployable AI service. That service ingests millions of documents, retrieves the right passages, calls model providers safely, streams answers in real time, and tracks its own quality, cost, and latency. Along the way you learn to handle the problems tutorials skip, the ones that break AI systems in production. A model returns malformed output. A provider rate-limits you at 2am. A job dies after four hours. A chunk boundary quietly causes a wrong answer. The bill arrives and nobody can explain it. A prompt change makes quality worse and nobody notices. Each module starts from a failure like these and builds the engineering that prevents it. By the end you will have written production-grade Python, with typed code, tests, observability, and a layered architecture. You will also understand why each piece exists, so you can make the same decisions on your own projects.

What you will learn

• Set up reproducible Python projects with modern tooling, type checking, and version control
• Turn loose data into typed, validated records and build pipelines that process millions of documents in constant memory
• Write resilient code that survives timeouts, rate limits, and provider outages with retries, circuit breakers, and fallbacks
• Validate model outputs, build tool-calling agents, and defend them against prompt injection
• Design provider-agnostic systems, and use databases, caches, and migrations correctly
• Use async concurrency and streaming to build fast, responsive AI services
• Build retrieval systems with chunking, embeddings, hybrid search, and reranking
• Count tokens, budget context windows, and control cost
• Test, evaluate, and monitor AI systems whose outputs change from run to run
• Structure and deploy a production-ready AI application

Included with the course

• 79 written parts, yours for good
• 7h 28m of reading, measured not estimated
• Written for the introductory level
• Every future revision included

Prerequisites

• Python: basic to intermediate knowledge. You should be comfortable with functions, loops, lists, dictionaries, and simple classes.
• Command line: able to navigate folders and run commands in a terminal.
• Good to have: some experience using Git, and familiarity with JSON and APIs.
• Not required: prior experience with machine learning, LLMs, or AI tools. Everything AI-specific is taught from the ground up.

Instructor

Reviews

Sign in to review this course once you have finished it.

Next to this one

Large Language Models80 parts · 27h 0m · FreeModel Context Protocol83 parts · 14h 44m · ₹199RAG82 parts · 13h 36m · ₹399