Vehicle Image Classification Using ML vs DL | Perceptron, CNN, Naive Bayes, Decision Tree Explained

🚗 Vehicle Image Classification: Machine Learning vs Deep Learning (ML vs DL) In this video, we explore a comparative study of traditional machine learning techniques and deep learning methods for classifying grayscale vehicle images into Vehicle and Non-Vehicle categories. 📊 Models Implemented: Single Perceptron Multi-layer Perceptron (MLP) Naive Bayes Classifier Decision Tree Classifier Convolutional Neural Network (CNN) 🔍 Key Concepts Covered: Dataset preprocessing and normalization Feature standardization PyTorch and Scikit-learn implementation Confusion matrices and model evaluation Accuracy comparison through visualization 📁 Dataset Source: Vehicle Detection Dataset (Kaggle) 🎯 Why it matters: This project has practical applications in traffic monitoring, surveillance, and autonomous vehicle systems. 🧠 Ideal for: Beginners in ML & DL AI project showcase Computer Vision learners 👉 Don’t forget to like, comment, and subscribe for more tutorials and project showcases! #MachineLearning #DeepLearning #VehicleClassification #CNN #PythonProjects #AI #ComputerVision #MLvsDL