Cats vs Dogs Classification using SVM with Python

🚀 Built my first AI system using linear algebra I built a movie recommendation system using cosine similarity and vector representations. Instead of directly using ML models, I focused on understanding how recommendation systems actually work under the hood. 💡 What I implemented: • Converted movie genres into feature vectors • Applied cosine similarity to measure similarity • Built a system that recommends similar movies 🧠 Key insight: Linear algebra concepts like vectors and similarity are the foundation behind real-world systems used by platforms like Netflix and YouTube. 🛠 Tech used: Python • Pandas • NumPy • Scikit-learn 🔗 GitHub: https://lnkd.in/gcAtQr6e #AI #MachineLearning #Python #DataScience #Projects #Learning

A few days back, I shared my first version of a Movie Recommendation System built using cosine similarity and genre-based filtering. At that point, it worked — but only at a basic level. Over the last few days, I tried improving it by: Integrating another dataset (Indian movies). Handling real issues like memory limits and data inconsistency. Moving beyond genres by adding movie overviews. Using TF-IDF to improve similarity. And honestly, one thing became very clear: 👉 Building something is easy 👉 Improving it is where real learning happens.

🚨 𝐀𝐈 𝐜𝐚𝐧 𝐝𝐞𝐭𝐞𝐜𝐭 𝐲𝐨𝐮𝐫 𝐬𝐭𝐫𝐞𝐬𝐬… 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐲𝐨𝐮 𝐬𝐚𝐲𝐢𝐧𝐠 𝐚 𝐰𝐨𝐫𝐝. We built a Stress Recognition System using Facial Landmark Annotation to capture micro-expressions and convert them into real-time stress levels. 🔍 𝐖𝐡𝐚𝐭 𝐰𝐞 𝐝𝐢𝐝: • 🎯 Tracked 468 facial landmarks (MediaPipe) • 📊 Labeled frames with 3D coordinates + stress intensity • ⚙️ Built an ML-ready dataset using OpenCV, Python, NumPy & Pandas 🧠  𝐌𝐨𝐬𝐭 𝐀𝐈 𝐝𝐞𝐭𝐞𝐜𝐭𝐬 𝐟𝐚𝐜𝐞𝐬. Very few understand human emotion. 👉 𝐖𝐡𝐞𝐫𝐞 𝐝𝐨 𝐲𝐨𝐮 𝐬𝐞𝐞 𝐭𝐡𝐢𝐬 𝐛𝐞𝐢𝐧𝐠 𝐮𝐬𝐞𝐝? #AI #ComputerVision #MachineLearning #EmotionAI #OpenCV #MediaPipe #teamwork

🚀 Day 8/30 – Image Transformations using OpenCV & Python 🐍📷 Day 8 of my 30 Days Python Challenge, and today I focused on strengthening my Computer Vision fundamentals ✨ I explored some essential image transformation techniques using OpenCV, including: ✨ Resize – changing image dimensions ✨ Crop – extracting a specific region ✨ Flip – horizontal and vertical transformations ✨ Rotate – rotating images at different angles ✨ Translation – shifting images across axes This hands-on practice helped me better understand how images are manipulated behind the scenes in real-world vision applications 💻 Every small concept is helping me build a stronger base for advanced OpenCV and AI projects 🚀 👉 Would love your feedback! 👉 Which image processing concept should I explore next? 😄 Day 9 coming tomorrow… stay tuned 👀 #Python #OpenCV #ComputerVision #ImageProcessing #30DaysChallenge #PythonProjects #AI #MachineLearning

🚀 Day 9/30 – Real-Time Image Filters using OpenCV 🐍📷✨ Day 9 of my 30 Days Python Challenge, and today I built a real-time image filter project using OpenCV 🎉 I experimented with different live filters, including: ✨ Original ✨ Grayscale ✨ Edge Detection ✨ Sepia ✨ Blur This hands-on project helped me understand how real-time frame processing and filter pipelines work behind the scenes in computer vision applications 💻 What I focused on today: ✨ Applying multiple filters in real time ✨ Live webcam frame processing with OpenCV ✨ Strengthening image processing fundamentals ✨ Exploring creative computer vision workflows This OpenCV streak is helping me move from basic transformations to visually engaging real-world projects 🚀 👉 Would love your feedback! 👉 Which filter should I add next? 😄 Day 10 coming tomorrow… stay tuned 👀 #Python #OpenCV #ComputerVision #ImageProcessing #30DaysChallenge #PythonProjects #AI #MachineLearning

Excited to share my latest project: Face Recognition Based Attendance System 🎯 Built using Python, OpenCV, and Machine Learning (KNN), this system detects faces in real-time and marks attendance automatically. 🔹 Real-time face detection 🔹 Automated attendance logging 🔹 Voice confirmation feature Looking forward to enhancing it further and exploring more in AI & Computer Vision! 🚀 #Python #OpenCV #MachineLearning #FaceRecognition #AI #Projects #Learning

🌸 GAMS is heading to National Harbor for #INFORMS2026 🌸 We’ll be back at the INFORMS Analytics+ Conference (April 12–14) with a hands-on workshop on building and solving optimization models in Python using GAMSPy, including how machine learning components can be embedded directly into those models. 📌 Bridging Optimization and Machine Learning: An Exploration with GAMSPy 📅 Sunday, April 12 | 1:00–2:45 PM 📍 Room: Camellia 1 Join Steve Dirkse and Adam Christensen for a practical walkthrough of GAMSPy, from core modeling concepts (sets, parameters, variables, equations) through to solving models and working with results in Python. The session also explores how structures like neural networks and regression trees can be incorporated into optimization models. Interested? Register here or DM us with any questions: 👉 https://lnkd.in/dSYEXuJ3 #INFORMS2026 #AnalyticsPlus #OperationsResearch #Optimization #GAMSPy #Python #MachineLearning

Excited to share my latest project: 𝐇𝐚𝐧𝐝 𝐆𝐞𝐬𝐭𝐮𝐫𝐞 𝐃𝐫𝐚𝐰𝐢𝐧𝐠 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 I built a real-time system that allows users to draw in the air using hand gestures - no mouse or keyboard required. This project is developed using Python, OpenCV, and MediaPipe, where the application captures live video, detects hand landmarks, and converts gestures into drawing actions. 𝑭𝒆𝒂𝒕𝒖𝒓𝒆𝒔: • Draw using finger movement • Change colors with gestures • Erase drawings using hand gestures • Supports both single-hand and two-hand modes 𝑻𝒆𝒄𝒉 𝑺𝒕𝒂𝒄𝒌: Python || OpenCV || MediaPipe || NumPy 𝑾𝒉𝒂𝒕 𝑰 𝒍𝒆𝒂𝒓𝒏𝒆𝒅: • Computer Vision fundamentals • Real-time gesture recognition • Working with pre-trained ML models 🎥 𝑫𝒆𝒎𝒐 𝒗𝒊𝒅𝒆𝒐 𝒂𝒕𝒕𝒂𝒄𝒉𝒆𝒅 𝒃𝒆𝒍𝒐𝒘 : 𝐆𝐢𝐭𝐇𝐮𝐛: https://lnkd.in/eRkUXtjb 𝘐 𝘸𝘰𝘶𝘭𝘥 𝘭𝘰𝘷𝘦 𝘵𝘰 𝘩𝘦𝘢𝘳 𝘺𝘰𝘶𝘳 𝘧𝘦𝘦𝘥𝘣𝘢𝘤𝘬 𝘢𝘯𝘥 𝘴𝘶𝘨𝘨𝘦𝘴𝘵𝘪𝘰𝘯𝘴! #Python #OpenCV #MediaPipe #ComputerVision #AI #Projects #Learning #Developer

🚀 Project Launch: MNIST Image Classifier (Handwritten Digit Recognition) I’m excited to share my latest Machine Learning project — an MNIST Image Classifier that can accurately recognize handwritten digits from images. 🧠 What the model does: • Takes an image of a handwritten digit (0–9) • Processes and normalizes pixel data • Predicts the correct digit using a trained ML model 📊 Key Highlights: • Trained on the MNIST dataset • Built an end-to-end ML pipeline (data preprocessing → model training → evaluation) • Achieved high accuracy on handwritten digit recognition 💡 Tech Stack: Python | NumPy | Scikit-learn / TensorFlow | Computer Vision 🖥️ Application: Developed a simple and user-friendly interface to test predictions in real time. This project helped me strengthen my understanding of image classification, data preprocessing, and building practical ML systems. I’d love your feedback! 🙌 #MachineLearning #ComputerVision #AI #Python #DeepLearning #StudentProject

Excited to share my latest project — an AI-Based Emotion Recognition System This application can analyze human emotions from voice using machine learning. Users can either record audio in real-time or upload a file to detect emotions like happy, sad, angry, and more. Built using Python, Streamlit, and audio processing techniques, this project reflects my passion for AI and real-world problem solving. GitHub: https://lnkd.in/dmQergwZ I’d love your feedback and suggestions! CodeAlpha #ArtificialIntelligence #MachineLearning #DeepLearning #DataScience #Python #Streamlit #AIProjects #EmotionRecognition #SpeechProcessing #AIInnovation #TechProjects #LearningJourney