Machine Learning (ML) research and academic projects focus on developing algorithms and models that enable systems to learn from data and improve over time. Key areas include supervised learning (label-based predictions), unsupervised learning (pattern discovery in unlabeled data), and reinforcement learning (decision-making via feedback). Research explores applications in image recognition, natural language processing, recommendation systems, and autonomous systems. Projects often involve data preprocessing, algorithm optimization, model evaluation, and deployment. ML also delves into neural networks, deep learning, and generative models. Academic initiatives aim to solve real-world prob
