Complete Python course for AI, ML and GenAI engineers
From your first print() to async LLM batching, RAG pipelines, and FastAPI serving. Every lesson has clear explanations, copy-paste examples, and a compiler built into the page.
Written for people targeting AI/ML engineer, GenAI engineer, MLOps and LLMOps roles — so every concept is tied to how it is used on the job, not toy exercises.
- 24
- Lessons
- 107+
- Code examples
- 8h
- Reading time
- ₹0
- Cost
What is inside
- +Real CPython in the browser — press Run, see output
- +Copy button on every single code block
- +NumPy and pandas load on demand in the compiler
- +Production files for PyTorch, FastAPI and LLM SDKs
- +Each lesson explains why it matters for AI work
- +Beginner to advanced in one ordered path
Four modules, 24 lessons, in order
Follow it top to bottom if you are new. If you already write Python, jump into Module 3 or 4.
Module 1 · Python foundations
Zero to comfortable: values, text, collections, and control flow.
- 01Why Python for AI, ML, and GenAIStart here. See why Python is the default language for machine learning and generative AI, how this course is organised, and run your first program in the on-site compiler.12 min
- 02Variables, types, and operatorsNames, numbers, booleans, and None — the values every hyperparameter, metric, and flag is built from.18 min
- 03Strings and textText is the raw material of NLP and GenAI. Learn quotes, f-strings, slicing, and the methods you will use on prompts and documents.20 min
- 04Lists, tuples, and setsOrdered collections and unique sets — how Python stores batches, token ids, labels, and vocabularies.20 min
- 05DictionariesKey–value maps are how Python represents JSON, API payloads, model configs, and metadata.18 min
- 06Control flow: if, for, whileDecisions and loops — training steps, early stopping, filtering predictions, and walking documents.20 min
Module 2 · Core language
Functions, generators, errors, files, classes, and project structure.
- 01Functions and argumentsPackage logic into reusable, testable units — the unit of work in every data pipeline and LLM wrapper.22 min
- 02Comprehensions and generatorsTransform collections in one readable line, and stream large datasets without loading them into memory.20 min
- 03Errors and exceptionsRead tracebacks, catch what you can handle, and fail loudly on everything else.20 min
- 04Files, JSON, and pathsRead and write data safely with context managers, and move between Python objects and JSON.20 min
- 05Classes and objectsBundle state and behaviour together — the pattern behind PyTorch modules, retrievers, and LLM clients.22 min
- 06Modules, packages, and virtual environmentsSplit code into files, import cleanly, and isolate dependencies so your project is reproducible.18 min
- 07Decorators and context managersWrap functions with reusable behaviour and manage resources cleanly — the mechanics behind @app.get, @task, and torch.no_grad().20 min
Module 3 · Python for data and ML
Type hints, NumPy, pandas, testing, logging, and configuration.
- 01Type hints and dataclassesDescribe the shape of your data so editors, reviewers, and mypy catch mistakes before runtime.20 min
- 02NumPy arrays and vectorsArrays, shapes, broadcasting, and cosine similarity — the numeric layer under every ML and embedding workflow.24 min
- 03pandas for datasetsLoad, filter, group, and clean tabular data — the step before every model and the place most data bugs live.24 min
- 04Testing ML code with pytestWrite tests that catch data and logic regressions, including the non-deterministic parts of ML systems.20 min
- 05Logging, configuration, and secretsReplace print with structured logs, read config from the environment, and keep API keys out of your repository.18 min
Module 4 · GenAI and production
Async, LLM APIs, tensors, RAG, FastAPI serving, and shipping habits.
- 01Async Python and concurrencyRun many slow I/O calls at once with async/await, and know when threads or processes are the right tool instead.22 min
- 02Calling LLM APIs in PythonHTTP, JSON payloads, structured output, token accounting, and cost control — the real mechanics of a GenAI integration.24 min
- 03From NumPy to PyTorch tensorsTensors, autograd, and a training loop — the Python patterns behind every model you will fine-tune.20 min
- 04Build a RAG pipeline in pure PythonChunk, embed, retrieve, rank, and assemble a grounded prompt — the whole retrieval pipeline with no frameworks.26 min
- 05Serving models with FastAPITurn a model or RAG pipeline into a validated, documented, containerized HTTP service.22 min
- 06Production Python for AI teamsTooling, project structure, CI, and the habits that separate a demo from a system people depend on.20 min
Who this is for
The same path works whether you are starting from nothing or filling gaps before an AI role.
Complete beginners
You have never written code. Module 1 assumes nothing and explains every term the first time it appears.
Career switchers
You come from support, testing, data entry, or another engineering field and need Python that stands up in an interview.
DevOps and cloud engineers
You know infrastructure and want the Python that MLOps and LLMOps roles actually ask for.
AI and GenAI engineers
You can already code but want the production side: async batching, structured output, RAG internals, and serving.
How the compiler works
The compiler is CPython compiled to WebAssembly, running inside your browser tab. Your code never leaves your machine, there is no account, and there is no server to rate-limit you.
Works offline after load
The runtime downloads once, then stays cached in the browser.
Standard library included
json, pathlib, dataclasses, asyncio, logging, itertools and more.
NumPy and pandas
Loaded on demand in the lessons that need them.
Limits worth knowing: no input(), no network calls, no GPU, and runs stop after 20 seconds. Lessons that need PyTorch, FastAPI, or an LLM SDK give you a complete file to run on your own machine.
Frequently asked questions
Is this Python course really free?+
Yes. Every lesson, code example, and the in-browser compiler are free to use with no signup. Paid live cohorts on MLOps, AIOps, and AI automation are separate programs.
Do I need to install Python to follow along?+
No. Each lesson has a compiler that runs real CPython in your browser through WebAssembly. Nothing is installed and nothing is sent to a server. For production work you will still install Python locally — the course shows you how.
Is this suitable for complete beginners?+
Yes. Module 1 starts from what a variable is. If you already write Python, skip to Module 3 for NumPy, pandas, testing, and logging, or Module 4 for async, LLM APIs, RAG, and FastAPI.
Which libraries can I run in the browser compiler?+
The full Python standard library, plus NumPy and pandas which load on demand. PyTorch, FastAPI, and LLM SDKs need a real machine, so those lessons give complete copy-paste files to run locally.
How long does the course take?+
About 8 hours of reading and running examples across 24 lessons. Most people spread it over two to four weeks alongside a job.
What should I learn after this course?+
Docker and Git, then a cloud platform, then the MLOps and LLMOps stack: MLflow, Kubernetes, vector databases, LangChain, and evaluation. The roadmaps on this site lay out the order.
Where to go after Python
Python is stage one. These free guides lay out the rest of the path into AI engineering roles.
Want the same depth on MLOps, LLMOps and AI agents?
This Python course is free forever. The live cohorts take you from Python into production ML and GenAI systems with mentorship and job support.
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