Master Python from Fundamentals to Production Engineering
Learn Python internals, backend development, async programming, testing, performance, data science, AI engineering, and technical interviews through visual lessons, code, labs, and real-world projects.
Overall Python progress
0 of 675 planned lessons
Lessons
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of 675
Labs
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Projects
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of 83
Best streak
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days
Skill scores across the curriculum
Role-based learning
Pick the path that matches your goal
Each path sequences the right tracks, projects and interview topics for a specific role — with a weekly plan and a job-readiness checklist.
Python Beginner
From zero to writing real programs with confidence.
~8 weeks · Beginner
Python Backend Engineer
Build production APIs with FastAPI, async and databases.
~16 weeks · Advanced
Python Automation Engineer
Automate the boring work reliably and safely.
~10 weeks · Intermediate
Data Scientist
Turn messy data into defensible insight.
~12 weeks · Intermediate
Machine Learning Engineer
Ship models as reliable, monitored services.
~16 weeks · Advanced
Generative AI Engineer
Build reliable LLM apps, RAG systems and agents.
~16 weeks · Advanced
Cloud & Platform Engineer
Automate infrastructure and platforms with Python.
~14 weeks · Advanced
Senior Python Engineer
Depth, architecture and system design for senior roles.
~20 weeks · Advanced
The curriculum
33 learning tracks
From your first variable to distributed AI systems. Every track carries interview and production importance so you can prioritise.
Foundations4 tracks
Python Fundamentals
Syntax, variables, dynamic and strong typing, I/O, operators, and the mental model of how a Python program runs.
Python Data Types
Numbers, strings, lists, tuples, sets and dicts — internal representation, complexity and the mistakes that bite in production.
Control Flow
Conditions, loops, comprehensions, pattern matching and guard clauses — with visual execution traces.
Functions
Arguments, scope and the LEGB rule, closures, recursion, first-class and higher-order functions, and the classic gotchas.
Core5 tracks
Modules, Packages & Imports
The import system, packages, sys.modules caching, circular imports, and how to expose a clean public API.
Collections
The collections module — deque, Counter, defaultdict, namedtuple, OrderedDict — and when each earns its place.
Object-Oriented Programming
Classes, inheritance vs composition, MRO, super(), dataclasses, properties, descriptors, slots and the magic methods.
Error & Exception Handling
try/except/else/finally, custom hierarchies, exception chaining and groups, and production-grade error design.
File & Data Processing
Text and binary files, CSV/JSON/YAML, pathlib, streaming large files, encodings and safe parsing.
Advanced5 tracks
Advanced Python
Iterators, generators, decorators, context managers, descriptors, protocols, metaclasses and structural pattern matching.
Functional Programming
Pure functions, immutability, map/filter/reduce, partials, composition and lazy pipelines — and when they pay off.
Iterators & Generators
The iterator protocol, generator suspension, yield from, lazy evaluation and streaming data pipelines.
Decorators & Metaprogramming
Closures, decorator factories, functools.wraps, class decorators, and real patterns: retry, cache, rate-limit, trace.
Context Managers
Resource lifecycle, with, __enter__/__exit__, contextlib, async context managers and ExitStack patterns.
Concurrency2 tracks
Async Programming
async/await, coroutines, tasks, the event loop, cancellation, task groups, backpressure and graceful shutdown.
Concurrency & Parallelism
Threads vs processes vs coroutines, CPU- vs I/O-bound work, locks, queues, pools, races and deadlocks.
Engineering11 tracks
Type Hints
Generics, Optional/Union, Protocols, TypedDict, overloads, ParamSpec, type guards, and mypy/pyright in CI.
Testing
Pytest fixtures, parameterization, mocking, async tests, coverage, and testing FastAPI, databases and LLM calls.
Debugging
Reading tracebacks, pdb, post-mortem and remote debugging, plus async, thread, memory and production debugging.
Performance Optimization
Measure first: benchmarking, cProfile, tracemalloc, caching, vectorization and choosing the right data structure.
Clean Code & Architecture
SOLID in Python, domain models, services, repositories, dependency injection and hexagonal/clean layering.
Database Engineering
SQL, transactions, isolation, indexing, SQLAlchemy 2.0, async sessions, Alembic migrations and Redis.
FastAPI Backend Engineering
Routing, Pydantic models, dependency injection, auth, middleware, lifespan, streaming, WebSockets and deployment.
Automation & Scripting
OS and file automation, shell commands, scheduling, email, spreadsheets, PDFs and robust CLI tools.
Cloud & DevOps
Docker, multi-stage builds, Gunicorn/Uvicorn, GitHub Actions CI/CD, and deploying to AWS and Google Cloud.
Security
Input validation, secrets, injection, deserialization risks, auth, crypto fundamentals and secure logging.
Packaging & Distribution
pyproject.toml, wheels, dependencies, versioning, entry points, publishing and reproducible environments.
Data & AI3 tracks
Data Science with Python
NumPy, pandas, cleaning, EDA, feature engineering, statistics and visualization with reproducible notebooks.
Machine Learning with Python
scikit-learn pipelines, training, validation, tuning, evaluation, persistence, serving and monitoring.
Generative AI with Python
LLM SDKs, structured output, tool calling, embeddings, RAG, agents, evaluation, tracing and cost control.
Career4 tracks
Data Structures & Algorithms
Pythonic implementations of lists, heaps, trees, graphs and tries plus the core algorithm patterns for interviews.
Python Interview Preparation
Role-based tracks, the advanced FAQ, layered model answers, mock interviews and a coding-practice engine.
Hands-On Labs
Guided, test-driven labs from a CLI calculator to an async producer-consumer, FastAPI CRUD and a full RAG pipeline.
Production Projects
Portfolio-grade builds — production FastAPI backends, RAG platforms, LLM gateways and multi-agent systems.
Not sure where to start?
Follow the interactive roadmap from installing Python all the way to shipping production services and specialising in backend, data, AI or the cloud.