What if a website could tell whether a resume belongs to a Data Scientist or an HR Manager in seconds? 👀 This semester, I had a subject called Natural Language Processing (NLP) and I was fascinated by how machines can understand human language. But I didn’t just want to study algorithms in theory. I wanted to see what NLP could do in the real world. That’s when I thought of recruitment. Every day, recruiters deal with hundreds of resumes and often, great profiles get lost simply because they weren’t tagged or sorted correctly. So I decided to try solving that with machine learning. __________________________________________________________________________________ 💡 The Idea: Build a Resume Screening Website that automatically classifies resumes into job categories like Data Science, HR, Web Development, and more. 📄 Upload a resume (PDF, DOCX, or TXT) ⚙️ The app extracts the text using TF-IDF vectorization 🤖 A Logistic Regression model predicts the job category 💻 Everything runs through a clean Streamlit UI 🧠 What I Learned: Turning academic concepts into real-world tools How data preprocessing can make or break an ML model Building and deploying interactive ML apps through Streamlit And the importance of designing AI systems that assist, not replace, human judgment. ✨ Why This Matters: Resume classification isn’t just about faster hiring. It’s about smarter talent management. Most companies have a massive database of past applicants of talented people who weren’t the right fit then but might be perfect now. With intelligent classification, recruiters can: 🔹 Re-screen stored resumes for new openings automatically 🔹 Rediscover candidates who already match new roles 🔹 Save time sourcing from scratch while reducing bias in early screening So instead of just filtering resumes, this system helps find opportunities for both recruiters and candidates 🌱 This project started as a semester assignment… but it ended as a kind of model that can help companies re-discover past applicants who might be a better fit for future roles. 💡 #MachineLearning #NLP #Python #Streamlit #RecruitmentTech #DataScience #OpenSource #CareerGrowth #LearningByDoing #AI #HRTech

Excited to introduce my new AI project -IntelliHire: Resume Ranking System, created with Python, NLP, and Streamlit. This tool compares resumes to job descriptions to deliver ranking scores, skill match insights, and AI-based recommendations, allowing recruiters to shortlist candidates faster. 🔗 Live App: https://lnkd.in/eqV5M_hQ 💡 Tech Stack: Python | NLP | Sentence Transformers | Streamlit | WordCloud | Matplotlib Proud to merge AI and automation to make hiring smarter and more efficient. #AI #MachineLearning #NLP #ResumeScreening #Streamlit #AIML #Python #RecruitmentTech

🚀 Road to Data Roles - AI Engineer Interview Series Begins The demand for AI Engineers is growing faster than ever from startups to global tech companies. But the interview process is becoming more challenging and more specialised. So today, I’m starting a new series: 🔥 Road to Data Roles: AI Engineer Interview Series This series will cover real AI Engineer interview questions with detailed explanations, including: 🔹 Machine learning fundamentals 🔹 Deep learning concepts 🔹 Neural network internals 🔹 LLMs (Transformers, Attention, embeddings, prompting) 🔹 Computer vision basics 🔹 NLP concepts 🔹 Model deployment & inference optimization 🔹 MLOps essentials 🔹 End to end AI system design 🔹 Coding and reasoning challenges 🔹 Real world scenario questions Each post will break down ONE interview question in depth, with: ✔ clear explanation ✔ examples ✔ real use cases ✔ mistakes to avoid ✔ how to answer confidently in interviews ✔ follow up questions a recruiter might ask Whether you're transitioning into AI engineering or preparing for interviews in 2025, this series will help you build true depth and confidence. Follow the hashtag : #RoadToDataRoles Let’s begin.

The AI landscape is evolving rapidly, and the demand for skilled professionals is skyrocketing. By 2026, we're looking at a 40-50% surge in AI job opportunities. Our latest video breaks down the top 5 game-changing skills every data professional needs: · Generative AI & Programming (Python, R, SQL) · Natural Language Processing · Data Engineering & Pipeline Design · Data Visualization & BI Tools · Cloud Computing (AWS, Azure, GCP) The best part is you can start building these skills TODAY. Watch the full video to discover how each skill can transform your career trajectory. Explore our comprehensive AI for Data Analyst course designed to equip you with practical, industry-relevant skills: https://tnvs.in/mr3ndc87 #ArtificialIntelligence #AISkills #DataAnalytics #CareerDevelopment #TechCanvass #AITraining #FutureOfWork #DataProfessionals #GenerativeAI #CloudComputing #NLP #DataEngineering

Excited to launch my first NLP project focused on HR & recruitment!! Built Streamlit app that automatically predicts job titles from job descriptions. -Solving a real HR challenge: turning raw job postings into meaningful categories to improve search -Instead of relying on predefined job categories, I clustered job titles using K-means (10 clusters) to create more meaningful, data-driven categories. -Data Handling & Feature Engineering: Used Title & Description columns, cleaned text, applied TF-IDF + K-means for new job title labels, and generated readable cluster names from top terms. -Preprocessing & Modeling: Tokenized text with TextVectorization (vocab=10k), used Embedding (128-dim) + RNN/LSTM/GRU architecture, applied Dropout (0.5) & Recurrent Dropout (0.3), optimized with Adam (loss: sparse_categorical_crossentropy), trained for 10 epochs (batch size 64). -Model Results: Tried different architectures — (RNN) – 55%, (LSTM) – 84%, Bidirectional LSTM – 83%, Bidirectional LSTM with GloVe Embeddings – 83%, (GRU) – 84%. -Next Steps: Integrate BERT/Roberta via transfer learning, apply data augmentation with job description templates, add Batch Normalization for stability, and enhance preprocessing with synonym replacement for domain-specific terms Try it live: https://lnkd.in/dKdu9-F4 #NLP #MachineLearning #DeepLearning #HRTech #JobTitlePrediction #Streamlit #TextClassification #RecruitmentTech

🚀 SkillBridge – AI-Powered Career Growth Platform Proud to share SkillBridge, my best and most impactful AI project till date! 💡 SkillBridge is an AI-powered resume–job matching system that helps professionals identify skill gaps and get a personalized 30/60/90-day learning plan to bridge them. It uses Natural Language Processing (NLP) to extract and compare skills from resumes and job descriptions — giving users clear, actionable insights to align their skills with their dream job. 💻 Tech Stack: Backend: FastAPI, spaCy, Sentence Transformers, pdfplumber, docx2txt, NumPy, regex Frontend: Streamlit, Pandas, PIL, Requests ⚙️ How It Works: 1️⃣ Upload your resume (PDF/DOCX/TXT) 2️⃣ Paste your target job description 3️⃣ Instantly get: ✅ Skills found in your resume ⚠️ Missing or weak skills with severity levels 📅 A personalized 30/60/90-day learning roadmap 🎯 Key Features: AI-based Skill Extraction Semantic Skill Matching using Transformers Skill Gap Analysis Personalized Learning Plan Generation Clean, modern Streamlit UI 💬 Note: Deployment is currently in progress 🚧 — facing a few technical issues, but it’ll be live soon! I’m excited to make SkillBridge accessible to everyone who wants to grow faster and smarter in their career. 🔗 GitHub: https://lnkd.in/ePpdmtdN 🎥 Demo Video: (Attached) #AI #FastAPI #Streamlit #Python #MachineLearning #NLP #CareerGrowth #ResumeAnalysis #DataScience #Innovation

🚀 Building My AI-Powered Resume Screening System As part of my MCA journey, I’ve started working on a Machine Learning + NLP-based Resume Screening System — a project that aims to make the hiring process faster and smarter. 🔍 What I’m Learning So Far: 1️⃣ How NLP (Natural Language Processing) helps machines understand text like humans. 2️⃣ The role of TF-IDF and Cosine Similarity in matching resumes with job descriptions. 3️⃣ Challenges of real-world data — messy resumes, mixed formats (PDF, DOCX), and long text. Understanding how to represent text as vectors and measure similarity. 🧠 Next Steps: 1️⃣ Experimenting with Sentence-BERT to improve contextual accuracy. 2️⃣ Adding a skill-based scoring module to make matching more interpretable. 3️⃣ Designing an interactive frontend interface (HTML + Flask) for real-time testing. 💡 I’m excited about how much this project is teaching me about AI applications in HR Tech — combining machine learning, NLP, and web development. #MachineLearning #NLP #Python #AI #DataScience #MCAProjects #ResumeScreening

🔥 AI professionals, this one’s for you. After mentoring, hiring, and connecting with hundreds of machine learning and data science professionals, I noticed one big challenge: everyone prepares hard, but very few prepare right. So I decided to change that. I’ve put together The Ultimate AI Interview Q&A Workbook, a 30+ page guide packed with everything you need to break into top companies and level up your technical confidence. Here’s a glimpse of what’s inside: 💡 100+ real FAANG and top-tier company questions with clear, structured answers 💡 Hands-on coding problems with Python solutions 💡 Deep dives into ML, DL, NLP, RL, and system design 💡 Real case studies from Google, Meta, Netflix, and Tesla 💡 Behavioral, SQL, and cheat sheets for that final interview polish This isn’t theory, it’s built for practical mastery. The kind of prep that gets you noticed by hiring managers and keeps you calm under pressure. 📘 You can download it for free and start today. If you’re serious about AI growth: ✅ Follow Sivaranjan A for weekly insights, AI projects, and advanced interview prep resources 🔁 Repost to help others in your network who are preparing too 💬 Comment “AI” and I’ll make sure you don’t miss future resources #ArtificialIntelligence #MachineLearning #DataScience #DeepLearning #FAANG #InterviewPreparation #CareerGrowth #AIInterview #MLOps #NLP #LinkedInTopVoice #JobSearch #TechCareers #AICommunity #Sivaaiexpert CareerByteCode

AI Project Update: Resume Skill Matcher 💫 Excited to share my latest mini AI project — “AI-Powered Resume Skill Matcher”💼 ➡️ This project uses Python, NLP, and Scikit-learn to analyze resumes and job descriptions, then calculates the skill match percentage 🔍. ➡️ It helps candidates identify how well their resumes align with job roles — a simple but powerful step toward smarter job matching! #AI #Python #MachineLearning #CareerTech #NLP #Flask #LearningInPublic

🚀 Excited to share my latest AI project! 🤖 I recently built an AI Job Role Prediction System using Python 🐍 + NLP + Streamlit, which predicts the most suitable job role based on the skills a user enters. 🔹 How it works: 👉 User enters skills like Python, SQL, Data Analysis 👉 The AI model (trained using TF-IDF + Naive Bayes) predicts roles such as Data Scientist, Backend Developer, DevOps Engineer, etc. This project helped me strengthen my skills in: ✅ Natural Language Processing (NLP) ✅ Machine Learning (scikit-learn) ✅ Web App Development with Streamlit ✅ Data Preprocessing & Model Deployment 💡 Tech Stack: Python | Streamlit | Pandas | Scikit-learn | NLP | Joblib I believe AI-based career guidance systems can help students and professionals better understand their strengths and align with the right job paths. 📈 Excited to enhance this further with a larger dataset, improved accuracy, and interactive UI! Would love to hear your feedback or suggestions! 💬 #AI #ArtificialIntelligence #MachineLearning #NLP #Python #DataScience #Streamlit #DeepLearning #MLOps #AITools #TechInnovation #AIProjects #PythonProjects #MLProjects #CareerPrediction #JobRolePrediction #FinalYearProject #DataMining #Coding #WomenInTech #ProjectShowcase #AICommunity #OpenSource #AITech #LearningJourney #100DaysOfCode #TechForGood #AIForEveryone #SkillMatching #StudentProject #AIEngineer #Innovation