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Keep shoppingPydantic Mastery: Python Data Validation & Modeling (2026)
36 students
Pydantic Mastery: Python Data Validation & Modeling (2026)
Master Pydantic from Basics to Advanced — Custom Validators, Serialization, Aliasing, and Secure Data Handling
Created byPratham Uday Chandratre
Last updated 5/2026
English
What you'll learn
- Build and validate Python data models using Pydantic for real-world projects, APIs, and data pipelines.
- Apply field constraints, type coercion, and optional fields to ensure clean, consistent, and error-free data.
- Implement nested models, lists, tuples, and custom validators for complex data structures.
- Serialize and deserialize Pydantic models for JSON, APIs, and configuration management in production.
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Course content
10 sections • 10 lectures • 2h 38m total length
Requirements
- Basic Python knowledge – Familiarity with variables, functions, and data types (strings, integers, lists, dictionaries).
- Python 3.8+ installed – Any IDE or code editor (e.g., VS Code, PyCharm, Jupyter Notebook) works.
- Willingness to learn – No prior experience with Pydantic is required. We start from the absolute basics.
Description
If you’ve ever struggled to validate, structure, and serialize data in Python, this course is your complete solution.
Pydantic has become the go-to library for developers who want fast, accurate, and reliable data models — whether for small scripts, complex backend systems, or production-grade APIs.
In Pydantic Mastery: Python Data Validation & Modeling (2026), you’ll progress from complete beginner to confident Pydantic pro. We start by comparing plain classes, dataclasses, and Pydantic models, so you’ll clearly understand why Pydantic exists and the situations where it outperforms traditional approaches.
What You’ll Learn:
Built-in field constraints: gt, min_length, regex, and more
Custom validators: @validator for single-field rules & @model_validator for cross-field validation
Serialization mastery: .model_dump() & .model_dump_json() for clean, structured output
Aliasing for smooth frontend/backend integration
Private attributes to protect sensitive data like passwords and tokens
Password strength enforcement using regex patterns
Real-world examples for API-ready, production-safe models
By the end of this course, you’ll be able to validate anything, serialize data like a pro, and build rock-solid data models — ready to plug into FastAPI, LangChain, LangGraph, or any modern Python project.
This is a hands-on, project-driven course. Every section includes assignments, quizzes, and coding challenges to reinforce your skills. Whether you’re a backend developer, data engineer, or AI enthusiast, this course will take your Python data modeling to the next level in 2026.
Who this course is for:
- Aspiring AI/ML Engineers & Data Scientists who want to build cutting-edge AI applications.
- Software Developers aiming to integrate LLMs into real-world projects.
- Tech Enthusiasts & Hobbyists curious about AI agents, prompt engineering, and automation.
- Entrepreneurs & Product Managers looking to create AI-driven products or enhance existing workflows.
- Researchers & Students exploring practical LLM implementations beyond theory.