- Developed a simple linear classifier and extended the project by implementing four classifiers: Logistic Regression, Perceptron, Support Vector Machine (SVM), and Softmax
- Applied classifiers to diverse datasets, including a binary classification task on the Rice dataset and a multi-class classification challenge with the Fashion-MNIST dataset
- Demonstrated adaptability by adjusting labels and parameter update rules to suit the specific characteristics of each dataset
- Gained hands-on experience in hyperparameter tuning and effectively partitioned data into training, validation, and test sets for robust model evaluation
- Strengthened understanding of classic machine learning methods and enhanced proficiency in scientific computing tools, particularly in Python