Decision Tree & K-NN Classifiers implementations for ML Course @ ITBA

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

Audio Pattern Recognition project - Music Genre Classification

Breast cancer data analysis and machine learning

Breast Cancer Prediction using K-NN, Machine Learning/Artificial Intelligence Algorithm on Python sckilearn

Testing face recognition power of different algorithms on ORL database.

This is a repository for implementing statistical learning models from scratch using the Python and Java programming languages.

I am partaking in research with my professor Dr. Boxiang Dong at Montclair State University in using deep learning techniques for anomaly detection. This project is to help with that research, specifically in implementing Machine Learning classifiers and more.

🔢 A self-introduction to machine learning. Simple application that recognises handwritten/mouse drawn digits from 0-9.

The k-NN tool is to provide a generic tool to conduct k Nearest Neighbour (k-NN) prediction of continuous forest target variables of interest. In the context of Ecosystem Restoration monitoring, the tool allows wall-to-wall propagation of the variables of interest using field reference data and provided EO datasets. Part of the PEOPLE-ER project.

Topographical Representation in an Associative Learning Task Using BSS Analysis on MEG Signals

Projeto desenvolvido durante a disciplina SIN 492 - Reconhecimento de Padrões, da Universidade Federal de Viçosa - Campus Rio Paranaíba.

Algorithms in Statistical Machine Learning and Data Mining

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

a machine learning training project

Machine Learning tasks and mini projects based on my learning in a Datascience bootcamp in Udemy

Recommendation system using k-Nearest Neighbors algorithm (k-NN).

Data Science, Algorithms, Notes, Learn

Sample of k-NN (K-Nearest Neighbor algorithm) by GoLang

Classification of Breast Cancer into Malignant or Benign type on the basis of computed features from a digitized image of a fine needle aspirate (FNA) of a breast mass.