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Introduction to FastAPI

IntermediateSkill Level
4.7+
1,322 reviews
Updated 10/2025
Build robust, production-grade APIs with FastAPI, mastering HTTP operations, validation, and async execution to create efficient data and ML pipelines.
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FastAPIProgramming4 hr10 videos29 Exercises2,300 XP13,124Statement of Accomplishment

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Course Description

Introduction to FastAPI and Its Applications

APIs are commonly used to manage data on the web, and FastAPI is a popular new web framework for building APIs with Python. Data and Machine Learning Engineers leverage FastAPI to build applications that serve as context-aware data transactions and pipelines on the web.

Building Production-Grade Web Applications with FastAPI

In this course, you will learn how to build production-grade web applications leveraging your Python skills with the FastAPI framework. By the end, you’ll understand core use cases for FastAPI in relation to other Python web frameworks. You’ll learn to build APIs using all four types of HTTP operations and define request and response schemas. In addition, you’ll validate API requests, respond with appropriate HTTP status codes and messages, and execute code asynchronously to improve application performance. A hands-on project will guide you through building a classical CRUD (Create, Read, Update, Delete) API, testing workflows using Python's requests module.

Course Goals and Target Audience

This course is designed for experienced Python programmers who want to build web applications that manage data using FastAPI. Upon completing this course, you will have the foundation necessary to create FastAPI projects suited for high-throughput data management and ML model-serving pipelines.

Prerequisites

Introduction to APIs in PythonIntroduction to Object-Oriented Programming in Python
1

FastAPI Basics

We’ll start by learning FastAPI’s key features and core use cases. Then we will run our first application and test it out! Finally, we will learn the details of supporting GET and POST operations that include request parameters and build and test those endpoints.
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2

FastAPI Advanced topics

We’ll start by learning how to support PUT and DELETE operations using FastAPI. Then we will learn how to handle different kinds of errors and always return an appropriate status code in the response. Lastly we'll learn how to use async to enable concurrent requests that can handle higher workloads.
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3

Building and testing a JSON CRUD API

We'll start by learning how to write system tests to validate individual FastAPI endpoints. Next we'll build a full JSON CRUD API to manage object lifecycles over HTTP. Finally, we'll learn how to test different application endpoints working together with manual functional tests.
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Introduction to FastAPI
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*4.7
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  • Ankit
    3 hours ago

  • Chaudhry Iftikhar
    yesterday

    Overall a good course to get most of the knowledge of FastAPI

  • Mari
    yesterday

  • Georg
    yesterday

  • Mher
    2 days ago

  • Frank
    2 days ago

Ankit

"Overall a good course to get most of the knowledge of FastAPI"

Chaudhry Iftikhar

Mari

FAQs

What prerequisites are needed to take this course?

This course requires intermediate-level Python knowledge. You should be comfortable with modules, packages, functions, and object-oriented programming, as well as have a basic understanding of APIs, Python's requests library, and handling JSON data.

Will I need to install any additional tools or libraries?

No specific installations are required before starting the course, as we will guide you through everything you need. However, you might still want to set up locally, so that you can create your own projects using FastAPI.

What is FastAPI, and why should I use it?

FastAPI is a modern, fast (high-performance) web framework for building APIs with Python. It’s especially useful for data and machine learning engineers due to its support for asynchronous programming, data validation, and production-grade application development. This course will help you understand how FastAPI compares to other frameworks and why it may be beneficial for building high-throughput data applications.

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