Basic Machine Learning implementation with python

The projects are part of the graduate-level course CSE-574 : Introduction to Machine Learning [Spring 2019 @ UB_SUNY] . . . Course Instructor : Mingchen Gao (https://cse.buffalo.edu/~mgao8/)

Logistic Regression technique in machine learning both theory and code in Python. Includes topics from Assumptions, Multi Class Classifications, Regularization (l1 and l2), Weight of Evidence and Information Value

Predicting gender of given Chinese names (93~99% test set accuracy). 预测中文姓名的性别(93~99%的测试集准确率)。

Using classic machine learning models - K-NN, Multiclass Logistic Regression, SVM and Random Forest to make predictions

Hand Written digit recognition by loading datasets from sklearn library

A multi-class classification problem where the objective is to read a question posted on the popular reference website, StackOverflow and predict the primary topics it deals with, i.e. tags which the question will be associated with.

Here we built a multinomial logistic regression classifier with scikit-learn. It takes numerical data of a bean an predicts which class does it belong to.

Multiclass Logistic, Classification Pipeline, Cross Validation, Gradient Descent, Regularization

Tensorflow codes written as part of Advanced Machine Learning Course Work

Wine Name Recognition using Logistic Regression

Employee Task management and review system for EinNell Expound Hackathon 2019

The project focuses on sentiment analysis of Coronavirus tweets NLP - Text Classification kaggle dataset

💵Model Peruvian Bills (MLR, Mask, Inceptionv2) RCNN💶

Multi class and Binary Classification through Logistic Regression and SVM

An NLP model that can predict the probability for each type of toxicity of comments.

A multi class persian text classification using logistic regression

Implementation of Trust Region and Gradient Descent methods for Multinomial Regression