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Machine Learning Image Recognition Service

Advanced 6-8 weeks Ai

Build an image recognition system that can identify objects, scenes, or faces in images. Create an API service that allows other applications to use this functionality.

Python TensorFlow/PyTorch FastAPI Docker AWS/GCP
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Project Overview

Key Features

  • Implement or fine-tune image recognition models
  • Create RESTful API for image processing
  • Build image preprocessing pipeline
  • Implement result caching and optimization
  • Create developer documentation and SDK

Learning Outcomes

  • Detailed learning outcomes will be provided upon enrollment.

Business Value

Image recognition is transforming industries from retail to healthcare to security. This project showcases your ability to work with advanced ML models, design scalable APIs, and solve complex computer vision problems - skills that command premium compensation in today's market.

Suggested Curriculum

  1. Implement or fine-tune image recognition models
  2. Create RESTful API for image processing
  3. Build image preprocessing pipeline
  4. Implement result caching and optimization
  5. Create developer documentation and SDK

Submission Requirements

  • Public GitHub repository with clean commit history.
  • README that explains features, setup, and deployment (template below).
  • Use semantic commits; no large binary files in Git.
  • Respect project structure and include environment variable samples.
  • Include screenshots or a short demo GIF in the README.
  • Pass basic linting and build checks; no console errors in UI.
Note: Do not include secrets in the repository. Use .env files locally and share example keys only.

Repository Standards

  • Default branch: main
  • Use a permissive license (MIT) unless otherwise needed
  • Include .gitignore for Node/Next.js
  • Add .nvmrc or engines field for Node 18+
  • PR-ready: clear folder structure and typed code (TS preferred)
  • No hardcoded credentials; use environment variables
  • Include sample data/seed script when relevant
  • Add basic tests where feasible (smoke tests acceptable)

Web Deployment Checklist

  • Hosted URL is mandatory for all web projects (Vercel recommended).
  • Ensure production build works (no build-time errors or 500s).
  • ENV vars configured on hosting platform; no secrets in code.
  • Update README with Live URL and deployment notes.
  • Basic SEO: title, meta description, favicon present.
  • Performance: images optimized, no blocking console errors.
Optional: Set up CI to run lint and type-check on pull requests.

README Template

# Machine Learning Image Recognition Service

A production-ready implementation of the Machine Learning Image Recognition Service project.

## Demo
- Live URL: <YOUR_DEPLOYED_URL>

## Features
- List the major features implemented

## Tech Stack
- Framework: Next.js / React
- Styling: Tailwind CSS
- State: React state / Zustand / Redux (if any)
- Other: List libraries

## Architecture
- Briefly describe folders and key modules

## Getting Started
### Prerequisites
- Node.js 18+

### Setup
```bash
npm install
```

### Environment Variables
Create a .env.local file using the template below and fill values:
```env
# .env.example
NEXT_PUBLIC_API_BASE=""
```

### Run Locally
```bash
npm run dev
```

### Build
```bash
npm run build && npm start
```

## Deployment
- Platform: Vercel / Netlify / GitHub Pages
- Build Command: npm run build
- Output: .next (default for Next.js)

## API Endpoints (if applicable)
- GET /api/... - description

## Screenshots
Include 2-3 screenshots or a short GIF demo.

## License
MIT

## Author
Your Name (@yourhandle)

Ready to Get Started?

Enroll in this project to access all resources and start building your portfolio with real-world experience.

Enroll Now
Advanced · 6-8 weeks

Project Includes:

  • Detailed documentation
  • Curriculum
  • Community support
  • Verified completion certificate

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