Tired of manually categorizing thousands of image reports, I built 'InsightSort'! This is a TensorFlow machine learning model that automatically classifies visual content with 95% accuracy. It saves serious time on moderation. Biggest lesson learned: The nightmare of data imbalance led me to master SMOTE techniques. Huge win for model robustness! Excited to open-source this next. Check out the architecture below! 👇 [Link to Project/GitHub/Demo] #MachineLearning #DataScience #Python #AI

Built a Video Feature Extraction Tool with Python 🧠 Just finished building a complete Video Analysis Tool that extracts key insights from any video — including: 🎬 Features Implemented Text Detection (OCR) — detects presence of on-screen text using Tesseract Motion Analysis — measures movement intensity via Optical Flow Object vs. Person Dominance — detects what dominates in scenes using YOLOv3 Visualization UI — built with Tkinter for simple upload and instant results 📊 The tool processes video frames in real-time and outputs structured JSON results — showing how machine learning, computer vision, and Python can work together to analyze visual data. 💻 Built with: Python, OpenCV, pytesseract, YOLOv3, and Tkinter Check out the short demo video 🎥 below to see it in action! --- 🔍 Use Cases Media analytics Video classification Scene summarization Visual AI research #Python #OpenCV #MachineLearning #ComputerVision #AI #DeepLearning #YOLO #Tesseract #Tkinter #VideoAnalytics #DataScience #Innovation #BuildInPublic #SoftwareDevelopment #Automation

🚀 Excited to share my latest project: Face Recognition System using LBPH and Haar Cascade with OpenCV! In this project, I built a robust system that can detect and recognize faces from images using Python. Here's what I accomplished: 1.Face Detection: Leveraged Haar Cascade Classifier for accurate face detection.   2.Face Recognition:Implemented the Local Binary Pattern Histogram (LBPH) algorithm to recognize faces in real-time.   3.Custom Dataset: Easily expandable to recognize multiple people by adding images in structured folders.   4.Confidence Scoring: Provides match confidence to indicate prediction reliability.   5.*Web Interface:Can be integrated with Streamlit or Flask for uploading test images.   6.Model Persistence: Save trained models and reuse them without retraining.  Technologies:*Python, OpenCV, NumPy, LBPH, Haar Cascade, Streamlit/Flask  #ComputerVision #Python #OpenCV #MachineLearning #FaceRecognition #AI #DeepLearning #Project

🎬 Movie Recommender System — Built with Streamlit & Machine Learning! Excited to share my latest project — a Movie Recommendation System that helps users find movies similar to their favorites! 🍿 🔍 How it works: The system takes a selected movie as input and recommends similar movies using a content-based filtering algorithm. It leverages machine learning to analyze movie features and find the best matches. 🧠 Tech Stack: Python 🐍 Streamlit (for the interactive web interface) Pandas, NumPy, Scikit-learn (for ML logic) Pickle (for model storage) TMDB API (for fetching movie posters 🎥) Try it live:https://lnkd.in/dQKu6XUq #MachineLearning #DataScience #Python #Streamlit #MovieRecommendation

Code Meet Intelligence: Day-4 🧠 Building a Private, Offline AI Search Engine! Forget keyword searching. This video dives into Semantic Search, showing how we built a custom engine that searches by meaning, not just text matches. We convert documents (including PDFs!) into Vector Embeddings using Sentence Transformers and index them with FAISS for ultra-fast retrieval. The demo proves the system's resilience by correctly answering a query even with a misspelling! This is the core technology behind internal knowledge bases and advanced RAG systems. #CodeMeetIntelligence #SemanticSearch #VectorDatabases #AIinSearch #SentenceTransformers #FAISS #MachineLearning #DeepLearning #Python #RAG

🪄✨ Invisible Cloak using OpenCV — Real-Time Color Disappearing Effect! Tried something fun and fascinating — created my own Invisible Cloak using Python and OpenCV! 🧥💻 🔹 How it works: - Captures live video and converts frames to HSV color space 🎥 - Detects the chosen cloak color dynamically using trackbars 🎨 - Replaces that region with the background to make it look invisible! 👻 - Added trackbars to easily change cloak color and fine-tune HSV ranges in real-time. 💡 Tech Stack: Python | OpenCV | NumPy 🎯 Concepts Used: Color masking, HSV thresholds, bitwise operations, and background segmentation Grateful to #InnomaticsResearchLabs and SAXON K SHA Sir for the constant guidance and inspiration 🙏 #OpenCV #Python #ComputerVision #InvisibleCloak #ImageProcessing #MachineLearning #InnomaticsResearchLabs #SAXON K SHA #Raghu Ram Aduri #AI #Innovation #RealTimeProcessing

Excited to share major project — Real-time Deepfake Video Detection Built an interactive Streamlet app that detects manipulated (deepfake) videos using a custom ResNet-50 model. It analyzes each frame in real-time, labels it as Real or Fake, and provides visual reports, statistics, and annotated videos. Key Features: Real-time uploaded video detection Confidence-based predictions and visualization Live charts and performance dashboard Downloadable CSV & annotated video results Easy-to-use interface with Streamlit + OpenCV + PyTorch Tech Stack: Python, PyTorch, OpenCV, Streamlit, Plotly, NumPy, Pandas Goal: Use AI to detect deepfakes and increase trust in digital content. #DeepLearning #ComputerVision #Streamlit #DeepfakeDetection #Project #GauravSharma

🖐💻 Hand Gesture Controlled Virtual Keyboard | Python + MediaPipe + OpenCV I’m excited to share my latest project — a Virtual Keyboard that works entirely through hand gestures! 👇 Using MediaPipe for hand tracking and OpenCV for real-time rendering, this project allows you to type on an on-screen keyboard simply by moving your fingers in front of your webcam — no physical keyboard needed. 🔍 Key Highlights ✅ Real-time hand landmark detection via MediaPipe ✅ On-screen virtual keyboard drawn with OpenCV ✅ Pinch gesture recognition to simulate key presses ✅ Works with any standard webcam ✅ Fully implemented in Python ⚙️ Tech Stack 🔹 Python 3.10+ 🔹 OpenCV 🔹 MediaPipe 🔹 NumPy 🧠 What I Learned - Gesture recognition using hand landmarks - Real-time computer vision pipelines - UI rendering in OpenCV - Calibrating physical gestures for digital input 🚀 Try it yourself Clone the project and run locally: 🔗 GitHub Repository: [https://lnkd.in/dbUaYkxm] 💬 Feedback Welcome I’d love to hear your thoughts or suggestions for improving it — such as adding voice feedback, better calibration, or multi-hand support. #Python #OpenCV #MediaPipe #ComputerVision #AI #MachineLearning #GestureRecognition #Innovation #ProjectShowcase

💻 Handwritten Digit Recognizer using KNN 🚀 Excited to share my latest ML project — a Handwritten Digit Recognizer built using the K-Nearest Neighbors (KNN) algorithm! 🧠 Tech Stack: Python 🐍 scikit-learn OpenCV Streamlit (for the web app interface) 🎯 About the Project: This app takes your handwritten digit as input (drawn on canvas) and predicts the correct digit using a KNN classifier trained on the Digits dataset from scikit-learn. 🔗 Try it here: 👉 https://lnkd.in/gXpC8RWM GitHub repo: https://lnkd.in/gMc3z3GN A small step in exploring Machine Learning and Model Deployment! ✨ #MachineLearning #KNN #Streamlit #AI #DataScience #Python #MLProjects