EasyTorch is a research-oriented pytorch prototyping framework with a straightforward learning curve. It is highly robust and contains almost everything needed to perform any state-of-the-art experiments.

An example of easytorch implementation on retinal vessel segmentation.

The project provides a complete end-to-end workflow for building a binary classifier in Python to recognize the risk of housing loan default. It includes methods like automated feature engineering for connecting relational databases, comparison of different classifiers on imbalanced data, and hyperparameter tuning using Bayesian optimization.

Several small AI projects, including basic machine learning algorithms, perceptron neural networks, convolutional neural networks, and semantic segmentation.

This project is an Android mobile application, written in Java programming language and implements a Recommender System using the k-Nearest Neighbors Algorithm. In this way the algorithm predicts the possible ratings of the users according to scores that have already been submitted to the system.

Fuzzy Systems Assignments (Classification and Regression) - TSK

my machine learning practices for my third year in MFCI CS department

this project is sentiment analysis about about Kampus Merdeka that launched at Youtube platform using Naive Bayes Classifier with TF-IDF term weighting, also get validated using K Fold Cross Validation. The score-mean result is 91.2%, pretty good for valid score.

Code and supplementary materials for the research paper titled 'Advancing Geological Image Segmentation: Deep Learning Approaches for Rock Type Identification and Classification', published in Applied Computing and Geosciences (Elsevier).

A notebook about commonly used machine learning algorithms.

Pada project ini, akan dilakukan identifikasi nilai mata uang rupiah dengan menggabungkan metode ekstrasi ciri Local Binary Pattern dan metode klasifikasi Naïve Bayes. Serta untuk pengukuran akurasi identifikasi dilakukan dengan metode evaluasi K-Fold Cross Validation. Dataset yang digunakan berupa citra dengan rincian terdapat 120 citra yang te…

This toolbox offers 6 machine learning methods including KNN, SVM, LDA, DT, and etc., which are simpler and easy to implement.

Machine Learning with MATLAB

MLB Team Runs Allowed Prediction Project (Linear Regression)

This toolbox offers 7 machine learning methods for regression problems.

A Java console application that implemetns k-fold-cross-validation system to check the accuracy of predicted ratings compared to the actual ratings and RMSE to calculate the ideal k for our dataset.

As part of this project, various classification algorithms like SVM, Decision Trees and XGBoost was used to classify a GPU Run as high or low time consuming process. The main purpose of this project is to test and compare the predictive capabilities of different classification algorithms

This repository contains a practical application of machine learning In Machine Learning Workshop at GDSC