Create AI-powered web applications with FastAPI and React. Discover Machine Learning with Python for Developers.
What you'll learn
- AI and Machine Learning Fundamentals with hands on
- Basic Programming in Python and Typescript
- Handle frameworks like FastAPI and React
- Build real world modern object recognition application
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Course Description:
(AI) Build an Object Recognition Web App with Python and React.
- Create AI-powered web applications with FastAPI and React. Discover Machine Learning with Python for Developers.
- This thorough course, (AI) Create an Object Recognition Web App with Python & React, is intended to provide developers with the knowledge needed to construct cutting-edge AI-powered apps. Students will learn how to build a full-stack object recognition web app using FastAPI, TensorFlow, and React, demonstrating the possibilities of machine learning in current web development.
- During this hands-on course, participants will delve deeply into both backend and frontend technologies, with a strong emphasis on Python for AI and backend development and TypeScript for frontend implementation. Essential for object recognition jobs.
***DISCLAIMER*** This course is part of a 2 applications series where we build the same app with different technologies including Angular, and React. Please choose the frontend framework that fits you best.
Students will next learn how to use the FastAPI framework to develop efficient and scalable REST APIs that act as the application's backbone. This part will go over subjects like request management, data validation, and asynchronous programming in Python to ensure that the backend can handle the demands of real-time object detection.
The course's core is its machine learning component, in which students will extensively use TensorFlow to construct and train unique object recognition models. Participants will learn how to prepare datasets, create neural network topologies, and tweak pre-trained models for peak performance. The training will also cover key issues including data augmentation, transfer learning, and model assessment methodologies.