The code for the ACL 2023 paper "Linear Classifier: An Often-Forgotten Baseline for Text Classification".

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Implementation of Parabel (Partitioned Label Trees for Extreme Classification) in Python

Лабораторные работы по предмету "Системы и методы принятия решений"

Implemented and compared three types of linear classifiers to use for sentiment analysis of Amazon product reviews.

Built classifiers using logistic regression and decision trees to classify product reviews and used machine learning techniques such as boosting, precision and recall, and stochastic gradient descent for optimization in Python

Contains code involving CS 503 - Lab 3 : Implementation of Support vector Machine.

Perceptron, Pseudo Inverse Linear Classifier, SGD, SVM, Batch Gradient Descent, Minibatch Gradient Descent with tensorflow

This project explores text classification using a dataset of AI-generated real and fake city facts. It evaluates the performance of simple linear classifiers.

Portfolio of Jupyter Notebooks demonstrating various ML models/concepts learned and developed during my graduate machine learning course and independently post-grad. Generally, a bottom-up modeling approach with Numpy is used to showcase grasp of mathematical foundation. Higher-level libraries (scikit-learn) used for optimized algo implementations.