SDP on Machine Learning For Image Classification

The program began with a prerequisite quiz on Python basics and machine learning, recognizing top performers Pranathi Vijaya Bharathi and Hrithik. On Day 1, students engaged in a hands-on activity, creating a dataset with 25 samples, 8 attributes, and 3 labels, and explored Kaggle for datasets. The widely-used Iris dataset was demonstrated, with a detailed explanation of its features and an introduction to essential libraries for data handling, visualization, and machine learning.

The day concluded with a quiz and practical exercises to prepare students for advanced topics. Day 2 focused on exploring key concepts like parameters, application domains, and terminologies through an interactive Salmon vs Seabass classification activity. Students trained a k-Nearest Neighbor (k-NN) model on the Iris dataset, visualized data, evaluated model performance using accuracy metrics, and reinforced their learning with a quiz. The program featured an inaugural address by Dr. K.M. Sadyojatha, HOD (ECE), who emphasized the importance of hands-on learning for industry readiness and innovation, and Dr. Naseeruddin, Assistant HOD, highlighted the role of student clubs in skill development. Student representatives shared positive feedback, and Mrs. Shilpa K. R., Assistant Professor, delivered the vote of thanks. This program offered a perfect blend of theory and practice, empowering students with essential skills in machine learning, data handling, and classification techniques, fostering deeper learning and career advancement.