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Python, FastAPI, ML & Agentic AI for .NET / C# Developers

Welcome! This repository is a production-grade, enterprise curriculum designed specifically for C# / .NET Engineers mastering Python Backend, Machine Learning, and Agentic AI Architecture.

Every single concept is anchored by drawing direct parallels to familiar .NET enterprise patterns (LINQ, IDisposable, TPL/ThreadPool, ASP.NET Core Minimal APIs & Controllers, Dependency Injection, EF Core, xUnit, Channels, OpenTelemetry, etc.).


📚 Master Curriculum Navigation (01 $\rightarrow$ 18)

🏛️ Track 1: Python Core, Concurrency & Idioms

# Module Folder Key Topics Covered C# / .NET Parallel
01 01_fundamentals/ Bytecode disassembly (dis), Type Hints (PEP 484), Mutability, == vs is, Caching/Interning, Copy vs DeepCopy, Core Libs (enum, pathlib, datetime, json, logging, os, sys), LEGB Scope, Mutable Default Traps Static Typing vs PEP 484, ReferenceEquals, Value vs Reference Types, Memory Model
02 02_data_structures/ Compact Dict Internals, Lists, Tuples, Sets, Comprehensions, Lazy Generators, collections (defaultdict, Counter, deque), itertools (islice, chain, pairwise), Pattern Matching Python's Native LINQ (.Select(), .Where(), .ToDictionary(), .GroupBy()), IEnumerable<T>, C# 9+ Pattern Matching
03 03_oop_and_dunders/ Classes, self, Class vs Instance Variables, __new__ Singleton, __slots__ Memory Optimization, Complete Dunder Protocol Matrix, @dataclass, Structural Duck Typing (typing.Protocol) ToString(), Equals(), GetHashCode(), Indexers, C# record, Duck Typing vs interface
04 04_advanced_mechanics/ Closure Late-Binding Trap, Parameterized & Class Decorators (@wraps), Context Managers (with, __enter__/__exit__), contextlib (suppress, ExitStack), yield from, Metaprogramming (__init_subclass__) using / IDisposable, yield return / IEnumerator, Action Filters & Aspect-Oriented Programming (AOP)
05 05_concurrency_and_async/ The GIL (Global Interpreter Lock), PEP 703 Free-Threading, CPU vs I/O Bound, multiprocessing, asyncio Single-Threaded Event Loop, asyncio.Semaphore, asyncio.timeout, asyncio.Queue CLR ThreadPool vs Python Event Loop, Task<T> vs Coroutine, Channel<T> vs Async Queue

🌐 Track 2: Backend APIs, Databases & Infrastructure

# Module Folder Key Topics Covered C# / .NET Parallel
06 06_pydantic/ Rust-powered Pydantic V2, Annotated types, Field & Model Validators, Discriminated (Tagged) Polymorphic Unions, SecretStr, from_attributes AutoMapper, camelCase Aliases, pydantic-settings C# DTOs + System.Text.Json + FluentValidation + IOptions<T> in one unified model
07 07_fastapi_fundamentals/ Routing, Clean Architecture (APIRouter), Hierarchical DI (Depends()), Yield dependencies, Lifespans, Middleware, JWT Bearer Auth, Streaming / WebSockets, UploadFile ASP.NET Core Minimal APIs / Controllers, IServiceCollection (AddScoped/AddSingleton), IHostedService, SignalR
08 08_database_sqlalchemy/ Async SQLAlchemy 2.0, Declarative Mappings (Mapped[...]), 1:N, N:M Relationships, N+1 Prevention (selectinload, lazy="raise"), Generic Repository & UoW, Connection Pooling, Async Alembic Migrations EF Core (DbContext, DbSet, LINQ queries, .Include()), dotnet ef migrations
09 09_redis_celery_and_observability/ Redis Cache-Aside with TTL, Distributed Locks, Celery Distributed Task Queues, OpenTelemetry (OTel Traces & Spans), Structured JSON Logging IDistributedCache, Azure Service Bus / RabbitMQ, OpenTelemetry .NET
10 10_testing_and_quality/ pytest, Fixtures, Dependency overrides, Async DB tests with transaction rollback, ruff & mypy tooling, Multi-stage Docker deployment (Gunicorn + Uvicorn workers) xUnit / Moq / WebApplicationFactory, Kestrel vs ASGI Multi-worker Architecture

📊 Track 3: Data Science, ML & Deep Learning

# Module Folder Key Topics Covered C# / .NET Parallel
11 11_numpy_pandas_and_polars/ NumPy ndarray, Vectorization, Broadcasting, Matrix Multiplication @, Pandas DataFrames/Series, GroupBy, Merges, Polars Rust-powered lazy DataFrames Span<T> / SIMD Vectorization, LINQ on DataTables, Apache Arrow
12 12_ml_and_pytorch/ Scikit-learn Pipelines, ColumnTransformers, Classification & Regression, Model Serialization (joblib), PyTorch Tensors, Autograd, nn.Module, DataLoader, Canonical Training Loop, model.eval() ML.NET, TensorPrimitives, Deep Learning Training Pipeline

🤖 Track 4: Modern AI, RAG & Agentic Ecosystem

# Module Folder Key Topics Covered C# / .NET Parallel
13 13_huggingface_and_embeddings/ Hugging Face Hub, transformers, Tokenizers (BPE), Dense Embeddings (sentence-transformers), Two-Stage Retrieval (Bi-Encoder + Cross-Encoder Rerankers) Semantic Kernel Embeddings, ONNX Runtime
14 14_rag_and_vector_databases/ Document AI (Docling / PyMuPDF / python-docx), Recursive Chunking, Vector Databases (Qdrant & pgvector), HNSW Indexing, Filtered Search, Grounded Prompt Synthesis Azure AI Search, Semantic Kernel RAG, Vector Search
15 15_llm_sdks_and_boto3/ Direct OpenAI SDK (AsyncOpenAI), Guaranteed Structured Outputs with Pydantic, Streaming Tokens, Anthropic Messages API & Prompt Caching, AWS boto3 Bedrock Converse API Azure OpenAI Client, AWS SDK for .NET
16 16_langgraph_and_mcp_agents/ LangGraph Cyclic State Machines, State & Reducers, Nodes, Conditional Edges, Checkpoints & PostgresSaver, Human-in-the-Loop (interrupt_before), Model Context Protocol (MCP) FastMCP Server Semantic Kernel Multi-Agent Orchestration, Workflow State Machines

🎯 Track 5: Interview Cheatsheet, Live Coding & System Design

# Module Folder Key Topics Covered Purpose
17 17_master_interview_prep/ Top 60 Curated Senior Python, AI & Agentic Backend Interview Questions across all 9 technical areas Rapid-review interview master cheatsheet
18 18_live_coding_and_system_design/ Live Coding Algorithms (LRU Cache, Token Bucket Rate Limiter, Deep Flattening, Min-Heaps, Async Batch Workers)
19_system_design_ai_and_backend.md: 4 Full System Design Case Studies
20_dotnet_to_python_interview_playbook.md: The .NET $\rightarrow$ Python Interview Pitch Playbook
Live coding mastery, system design blueprints, and behavioral interview narrative

🚀 Quick Start: Running Tests Across All Modules

Run all interactive verification tests with one command:

pytest

Or run module exercises individually:

python 01_fundamentals/exercises.py
python 02_data_structures/exercises.py
python 03_oop_and_dunders/exercises.py
python 04_advanced_mechanics/exercises.py
python 05_concurrency_and_async/exercises.py
python 06_pydantic/exercises.py
python 07_fastapi_fundamentals/exercises.py
python 08_database_sqlalchemy/exercises.py
python 09_redis_celery_and_observability/exercises.py
python 10_testing_and_quality/test_demo_api.py
python 11_numpy_pandas_and_polars/exercises.py
python 12_ml_and_pytorch/exercises.py
python 13_huggingface_and_embeddings/exercises.py
python 14_rag_and_vector_databases/exercises.py
python 15_llm_sdks_and_boto3/exercises.py
python 16_langgraph_and_mcp_agents/exercises.py
python 18_live_coding_and_system_design/exercises.py

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A practical journey into Python from the perspective of a C#/.NET developer.

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