Skill bridge

Inspiration

As a tech enthusiast constantly learning and exploring fields like AI, Web Development, and Data Science, I noticed a recurring problem—students and early professionals struggle to identify which skills are trending and what to learn next. There was no unified system that could analyze a learner’s profile and suggest customized learning paths based on real-time industry demands.

That's where the idea of SkillBridge was born: To create an intelligent platform that bridges this gap by helping users analyze their current skills, discover trending technologies, identify personal skill gaps, and receive personalized course recommendations.

What it does

SkillBridge acts as a smart mentor for learners by:

Analyzing a user's skill set based on manual input and resume parsing

Matching skills with trending job market data (scraped from LinkedIn, Naukri, etc.)

Recommending courses based on the user's current level, learning goals, and interests

Tracking learning progress and suggesting periodic upskilling tasks

Using ML/NLP to personalize insights and recommendations dynamically

How we built it

Frontend React.js with MUI for responsive UI

Redux Toolkit for managing state

Firebase Auth for login/signup

Backend Node.js and Express.js to build RESTful APIs

MongoDB Atlas to store user profiles and skill/course data

JWT for secure session handling

AI/ML Layer Python (FastAPI) to run the AI models

NLP using NLTK & Spacy to parse resumes and extract keywords

Recommendation Engine using Scikit-learn & TensorFlow

Hosting Vercel for frontend

Challenges we ran into

Cleaning and processing real-world job data for skill matching

Coordinating async data flow between frontend and ML APIs

Evaluating and tuning ML models for accurate recommendations

Implementing secure, token-based auth across microservices

Designing a UI that's both intuitive and info-rich for different user types

Accomplishments that we're proud of

Built a fully functional end-to-end skill analysis and recommendation platform

Integrated machine learning with real user data in a production-ready setting

Successfully scraped and analyzed job trend data to inform learning paths

Designed a plug-and-play AI API that could be reused in future ed-tech apps

What we learned

How to build intelligent systems that merge frontend UX with backend ML logic

Real-world use of NLP in resume parsing and skill extraction

Data-driven decision making for personalized user recommendations

Handling token-based authentication and authorization across services

How to keep users engaged through smart UI/UX decisions

What's next for Skill bridge

Peer learning groups based on shared interests and goals

Real-time job alerts based on upskilling progress

Improved AI models using deep learning and collaborative filtering

Built With

• lstm
• ml
• natural-language-processing
• react
• restapi