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Basic Machine Learning implementation with python
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)。
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
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/)
Implementation and analysis of core Machine Learning Algorithms from scratch.
Multi class and Binary Classification through Logistic Regression and SVM
Using classic machine learning models - K-NN, Multiclass Logistic Regression, SVM and Random Forest to make predictions
Image Classification, Image Feature Extraction, CNNs, Finetuning, Resnet18, Torchvision, Multi-Class Logistic Regression
An NLP model that can predict the probability for each type of toxicity of comments.
This is the term project for the Mathematical Foundations of Data Science course in Bilkent University. The aim of this project is to automatically diagnose skin cancer from images.
Multiclass logistic regression implementation from scratch
We investigated the performance of the Logistic and Multiclass Regression models and compared their accuracies to KNN. We compared Logistic Regression and KNN based on the "IMdB reviews" dataset, while Multiclass Regression and KNN were compared based on the "20 news groups" dataset.
Logistic Regression is a supervised learning algorithm that is used when the target variable is categorical. In Logistic Regression the target variable is categorical where we have to strict the range of predicted values. Consider a classification problem, where we need to classify whether an email is a spam or not. So we have to predict either 0 (for not spam) or 1 (for spam).
Machine Learning Predictive model - Logistic Regression on the quality of red wine
:dollar:Model Peruvian Bills (MLR, Mask, Inceptionv2) RCNN:euro:
Investigated how multi-class logistic regression would perform if the activation function was changed from softmax to sigmoid. It included mathematical analysis and empirical evaluation, such as rewriting the model from scratch. Tech: Python (Scikit-Learn, Pandas)
Employee Task management and review system for EinNell Expound Hackathon 2019
My implementation of homework 2 for the Machine Learning class in NCTU (course number 5088).
Hand Written digit recognition by loading datasets from sklearn library
- Built a deep learning model from scratch, used the mathematical equations to implement algorithms in the model
Multilabel Classification of Wikipedia Talk comments for calculating Toxic behavior.
Image prediction model with logistic regression multiclass model, ML library as sklearn, Matplotlib using Python.
The project focuses on sentiment analysis of Coronavirus tweets NLP - Text Classification kaggle dataset
Multiclass Logistic, Classification Pipeline, Cross Validation, Gradient Descent, Regularization
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.
Image Recognition using Python on MNIST dataset with the help of CNN, Multiclass Logistic Regression and SGD
Machine Learning course instructed by Dr. Riahi, Fall 2023, Shahid Beheshti University
Handwritten Digit Recognition by 2 methods: - Multi-class classification (oneVsAll) - Neural Network ---- OCTAVE -- the exercise details are in ex3.pdf
A multi class persian text classification using logistic regression
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.
Tensorflow codes written as part of Advanced Machine Learning Course Work
Wine Name Recognition using Logistic Regression
Multiclass Logistic, Classification Pipeline, Cross Validation, Gradient Descent, Regularization