Can a computer recognize your mood? Yes! I just built a Binary Image Classifier that predicts whether you're Happy or Not Happy using a custom-trained CNN. With Gradio integration, it works like a mini web-app—just upload your image and get instant results. Using TensorFlow, Keras, OpenCV, and CNN architecture, the model was trained on custom datasets with multiple convolution + max-pooling layers. I also integrated the model with Gradio to deploy an interactive web UI, allowing users to upload images and instantly get mood predictions. Tech Stack: ✔ TensorFlow, Keras ✔ CNN (Conv2D, MaxPooling, Flatten, Dense) ✔ OpenCV ✔ ImageDataGenerator (Data Preprocessing) ✔ Python ✔ Gradio for UI Deployment #AIProjects #ComputerVision #Python #DeepLearningJourney

🎯Project Drop: Real-Time Emoji Face Detector 😄😢😠😲 Ever wished your computer could read your emotions and respond with an emoji? Well… I built one! 🤖✨ Using Python, OpenCV, and DeepFace, I created a real-time Emoji Face Detector that captures facial expressions through your webcam and instantly overlays matching emojis on your face! Whether you’re 😄 smiling, 😢 sad, 😠 angry, or 😲 surprised — it reacts instantly. 📂 GitHub Repository: 👉 https://lnkd.in/dkukUx3Y A perfect mix of Machine Learning + Fun! 🔧 Tech Stack: Python | OpenCV | DeepFace | Computer Vision 💬What’s your current emoji mood right now? 😄😢😠😲 Tell me below ⬇️ #Python #DeepLearning #MachineLearning #ComputerVision #OpenCV #DeepFace #AI #Projects #Portfolio #FunWithTech #Innovation

Every iteration makes the model more accurate — and me, a little better at building it. 💻I’ve been developing an intrusion detection model that focuses on identifying unusual network activity through data-driven analysis. The work mainly involves Python, NumPy, Pandas, and Scikit-learn, along with some ML based techniques for pattern detection and classification. Most of my time goes into data preprocessing and experimenting with different model architectures to understand which approach performs best. Along the way, I’ve run into multiple errors and inconsistencies — especially during model evaluation and tuning — but each issue helps me understand how the data and algorithms behave in practical use. Right now, I’m refining the pipeline to make it more efficient and exploring ways to improve detection precision while keeping false positives low. It’s still a work in progress, but the process itself has been a great deep dive into how applied ML systems actually evolve. #MachineLearning #Python #IntrusionDetection #AI #NetworkSecurity #TechProjects

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

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

I built an AI tool in Python that summarizes NFL games from play-by-play data. It leverages LLM and structured data analysis to process selected events and generate human-readable/data-driven summaries. This solution can be applied to any structured dataset, turning complex data into actionable insights. #python #AI #NFL

📘 Resource Recommendation: Understanding Vector Embeddings in AI A very insightful session by Pamela Fox that demystifies vector embeddings and their role in modern AI systems. 🎥 Watch here: https://lnkd.in/e9mwTMdA In just one hour, the session covers: 🔹 How vector embeddings work across models 🔹 The idea of similarity space 🔹 Vector search — Exhaustive vs ANN (HNSW, DiskANN) 🔹 Quantization (Scalar, Binary) 🔹 MRL dimension reduction 🔹 Compression with rescoring The accompanying Python notebooks allows for practical experimentation — ideal for those who want to go beyond theory. This session is part of the broader Python + AI series. You can explore more recordings here: 📌 https://aka.ms/PythonAI/2 #AI #MachineLearning #Python #VectorSearch #Embeddings #MicrosoftAI #TechLearning

Exploring the Power of AI in Audits: Post #3 Transform your audit workflow with Python + AI ✅ Beginner-friendly setup guide ✅ Ready-to-use code templates ✅ Step-by-step implementation From automated invoice processing to intelligent anomaly detection—all explained in plain language.