Plain python implementations of basic machine learning algorithms

Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation

Fast and lightweight header-only C++ library (with Python bindings) for approximate nearest neighbor search

My solution to TUM's Machine Learning MNIST challenge 2016-2017 [winner]

This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.

In This repository I made some simple to complex methods in machine learning. Here I try to build template style code.

A toolkit for organizing, cleaning and analysing your image datasets.

k-NN-based mapping of cells across representations to transfer labels, embeddings, and expression values.

An implementation of the K-Nearest Neighbors algorithm from scratch using the Python programming language.

Driver drowsiness is one of the causes of traffic accidents. According to the statistics; highway road crashes hold 11.09% of the total number of accidents. There are several reasons of drowsy driving such as: a lack of quality of sleep, may be overnight driving or having sleep disorders e.g. sleep apnea. However; all people should know that: People can not fight against to sleep. Using Image Processing and both classical and new-brand Machine Learning techniques, we are trying to know beforehand the driver's drowsiness and warning him/her with an alert before any crash happened.

A robust classifier for few-training-data problem based on a distributionally robust optimization framework

Tour of Machine Learning Algorithms for Binary/Multiclass Classification

Nearest neighbor search. Methods: LSH, hypercube, and exhaustive search. C++

TFG realizado en la Universidad de Burgos del desarrollo de una aplicación para el uso de un Radar de 60 GHz de la marca Acconeer.

GeoAdEx: A geometric approach for finding minimum-norm adversarial examples on k-NN classifiers

Web interactive streamlit dashboard for credit scoring interpretation

PyTorch implementation of following: Transfer Learning, Feature Extraction from deep network, k-NN

DCAi: Machine Learning Based DCA Strategy

Fedrann: a scalable pipeline for overlap detection based on large-scale sequencing data.

Audio Pattern Recognition project - Music Genres Classification

Diverse algorithms related to Machine Learning

A basic fruit sorter using k-means and k-nn.

Clasificador de imagenes de bananas, naranjas y limones por medio de algoritmos de aprendizaje K-nn y K-means. Procesado de imágenes con SciKit y OpenCV.

Simpsons Members Recognizer Supervised Machine Learning Algorithm.

Built a voice-controlled car from scratch incorporating machine learning methods such as the Euclidean Classifier and k-NN Classifier, open and closed loop feedback control systems, principal component analysis, regression analysis, and transient analysis

This machine learning initiative seeks to leverage the k-Nearest Neighbors (k-NN) classification algorithm to predict whether a Universal Bank will accept a personal loan offer.

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

Gained insights into the New York City Airbnb rental properties and concluded the neighbourhoods with most attractive Airbnb rentals and the type of rental properties with most reviews. Furthermore, concluded the economic viability of the rentals with missing reviews through machine learning models such as k-NN, decision tree and gradient boosted tree (GBT) classifiers implemented via data science platform RapidMiner.

KNN Is A Machine Learning Algorithm For Pattern Recognition That Finds The Nearest K Observations To Predict A Target.

This program is a real-time face recognition system that uses OpenCV and k-Nearest Neighbors (k-NN) to detect and label faces from a webcam feed.