[Ai] Create A Object Recognition Web App With Python & React

Build AI-driven web apps 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

Requirements

No programming experience required. Only computer and access to internet

Description

[AI] Create a Object Recognition Web App with Python & ReactBuild AI-driven web apps with FastAPI and React. Discover Machine Learning with Python for developers.This comprehensive course, "[AI] Create a Object Recognition Web App with Python & React," is designed to empower developers with the skills to build cutting-edge AI-powered applications. By combining the power of FastAPI, TensorFlow, and React, students will learn to create a full-stack object recognition web app that showcases the potential of machine learning in modern web development.Throughout this hands-on course, participants will dive deep into both backend and frontend technologies, with a primary focus on Python for AI and backend development, and TypeScript for frontend implementation. The course begins by introducing students to the fundamentals of machine learning and computer vision, providing a solid foundation in AI concepts essential for object recognition tasks.Students will then explore the FastAPI framework, learning how to create efficient and scalable REST APIs that serve as the backbone of the application. This section will cover topics such as request handling, data validation, and asynchronous programming in Python, ensuring that the backend can handle the demands of real-time object recognition processing.The heart of the course lies in its machine learning component, where students will work extensively with TensorFlow to build and train custom object recognition models. Participants will learn how to prepare datasets, design neural network architectures, and fine-tune pre-trained models for optimal performance. The course will also cover essential topics such as data augmentation, transfer learning, and model evaluation techniques.On the frontend, students will utilize React and TypeScript to create a dynamic and responsive user interface. This section will focus on building reusable components, managing application state, and implementing real-time updates to display object recognition results. Participants will also learn how to integrate the frontend with the FastAPI backend, ensuring seamless communication between the two layers of the application.Throughout the course, emphasis will be placed on best practices in software development, including code organization and project structure. Students will also gain insights into deploying AI-powered web applications, considering factors such as model serving, scalability, and performance optimization.By the end of the course, participants will have created a fully functional object recognition web app, gaining practical experience in combining AI technologies with modern web development frameworks. This project-based approach ensures that students not only understand the theoretical concepts but also acquire the hands-on skills necessary to build sophisticated AI-driven applications in real-world scenarios.Whether you're a seasoned developer looking to expand your skill set or an AI enthusiast eager to bring machine learning models to life on the web, this course provides the perfect blend of theory and practice to help you achieve your goals in the exciting field of AI-powered web development.***DISCLAIMER*** This course is part of a 3 applications series where we build the same app with different technologies including Angular, React and a cross platform Mobile App with React Native CLI. Please choose the frontend framework that fits you best.Cover designed by FreePik

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 AI, Machine Learning and Deep Learning

Lecture 3 Convolutional Neural Networks (CNNs)

Lecture 4 Installing VSCode

Lecture 5 VSCode Extensions

Lecture 6 Best way to take advantage of this course

Section 2: FastAPI and Python Setup

Lecture 7 What is Python and FastAPI?

Lecture 8 Installing Python for MacOS

Lecture 9 Installing Python for Windows

Lecture 10 Installing and running FastAPI

Lecture 11 Another Example Route

Lecture 12 Running the server with Uvicorn

Lecture 13 Installing packages using requirements.txt

Section 3: React Application Setup

Lecture 14 What is React and Typescript?

Lecture 15 Install NodeJS

Lecture 16 Create First React App with Vite

Lecture 17 ImageControl Component and Style

Lecture 18 Setting State Variables

Lecture 19 Predictions and Image Boxes Template

Lecture 20 Image Upload Input

Section 4: Creating and Setting Prediction Model

Lecture 21 Explaining TensorFlow, SSD Model and Coco Dataset

Lecture 22 Adding MobileNetV2 SSD COCO Model DataSet

Lecture 23 Loading Pre-Trained Model into our App

Lecture 24 Run Inference Function

Lecture 25 Predict Route

Lecture 26 Label Map

Lecture 27 Returning Results From Prediction Route

Lecture 28 Testing Predict Route

Section 5: Adding Serve Data to FrontEnd

Lecture 29 UseUploadImageHook

Lecture 30 Result Types

Lecture 31 Returning Data from Hook

Lecture 32 Using Hook in Image Control

Lecture 33 API Key

Lecture 34 HandleUpload and HandleImage

Lecture 35 Testing Image Upload

Lecture 36 Allow CORS

Lecture 37 Getting Results into Screen

Section 6: Additional Lectures

Lecture 38 Splitting FrontEnd into smaller components

Lecture 39 React Props

Lecture 40 Use cases and limitations

Beginner Python, Frontend and AI developers. Students with interest in how AI works