The Complete Python Curriculum
Everything Python in this course, under a single link — from absolute basics to expert real-world engineering, then a full computer-science Data Structures & Algorithms track, and finally the advanced AI-engineering layer. Work top to bottom for a complete path, or jump to any part. Every page is hands-on and runnable, and each concept links to where the course actually uses it.
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① Python Fundamentals P1–P6 · basics → expert
Python basics
Running Python, variables & types, strings & f-strings, booleans, control flow, functions, numbers, truthiness, the main guard.
● ReadyData & structures
Lists, dicts, nested data, tuples & sets, comprehensions, JSON & files, regex, sorting, enumerate/zip, Counter/defaultdict.
Functions, modules & OOP
Dispatch, *args/**kwargs, modules & imports, type hints, classes, inheritance, dunders, dataclasses, enums, ABCs.
Advanced for agents
Pydantic, exceptions, context managers, generators, decorators, async intro, environment & secrets handling.
● ReadyExpert Python
Advanced typing, functools/itertools, Pydantic mastery, structured concurrency, protocols & dunders, testing LLM code by mocking.
Real-world engineering
Memory & performance, resource management, rate limiter + circuit breaker, config & JSON logging, real pytest, packaging, quality toolchain.
● Ready② Data Structures & Algorithms D1–D6 · CS from scratch
Complexity & arrays
Big-O/Θ/Ω, counting operations, space complexity, dynamic arrays & amortization, two-pointer, sliding window, prefix sums.
● ReadyStacks, queues & linked lists
Stack, queue, deque, priority queue/heapq, singly & doubly linked lists, cycle detection, an O(1) LRU cache.
● ReadyHashing, sets & recursion
Hash tables from scratch (chaining + open addressing), dict/set internals, recursion, memoization/DP, backtracking.
● ReadyTrees & heaps
Binary trees, traversals (DFS/BFS), BST, balancing (AVL/red-black), a heap from scratch, tries, segment/Fenwick trees.
● ReadyGraphs
Representations, BFS/DFS, topological sort, Dijkstra, Bellman-Ford, minimum spanning trees & union-find.
● ReadySorting & searching
Every sort (bubble→quick/merge/heap/counting/radix), stability & Timsort, binary search & variants, quickselect.
● ReadyLinked list patterns
Every list type (singly/doubly/circular) & manipulation pattern — dummy head, reverse whole/between/K-group, fast-slow (middle/nth/cycle), merge, add, palindrome, reorder, intersection, copy-with-random — with pointer diagrams.
● ReadyBig Tech DSA patterns
The recognizable patterns behind Big Tech coding rounds — sliding window, two-pointer, top-K heap, binary-search-on-answer, intervals, grid BFS/DFS, trees/LCA, backtracking, DP, monotonic stack — with 40+ worked problems & diagrams.
● Ready③ Advanced AI Engineering A1–A8 · production layer
Dynamic Python mechanics
Object model, __getattr__/__call__, descriptors, stateful decorators, metaclasses, dynamic tool registry & plugin loading.
Memory & concurrency / the GIL
__slots__, buffers & zero-copy, GC/weakref, the GIL explained, threads vs processes vs async — chosen by bottleneck.
High-throughput async & streaming
Event loop, coroutines, gather/TaskGroup, semaphores/backpressure, timeouts, token streaming, queue pipelines.
● ReadyNumerical & tensor computing
numpy vectorization & broadcasting, cosine top-k, SciPy, a tiny autograd engine, dtypes/quantization, local LLM inference.
● ReadyStrings, tokenization & parsing
Unicode/encodings, normalization, BPE from scratch, token counting, advanced regex, chunking, JSON/CSV/pandas.
● ReadyValidation & resiliency
Pydantic mastery, discriminated unions, structured LLM outputs, error taxonomy, retry/backoff, circuit breaker, idempotency.
● ReadyVector DBs & frameworks
Embeddings, exact vs ANN (HNSW/IVF/PQ), vector databases, hybrid search + RRF + rerank, LangChain vs LlamaIndex architecture.
● ReadyMLOps, deployment & tracking
Lifecycle, experiment tracking, prompt/model registry, packaging & serving, eval-gated CI/CD, deploy strategies, monitoring & drift.
● ReadyBig Tech AI-engineering patterns
The AI/ML interview loop — implement softmax, attention, kNN/k-means & top-p sampling from scratch, plus design rounds for RAG & agent systems, concurrency, and productionization. With code & diagrams.
● Ready