Learning to create Machine Learning Algorithms

A simple K-Means Clustering model implemented in python

Clustering a set of word/tags using K-Means with word2vec or wordnet distance

Information retrieval course project

Using K-means Clustering for Image Compression

Clustering similar tweets using K-means clustering algorithm and Jaccard distance metric

This repository is a collaborative work towards creating a serverless application called Learning Management System. This application follows multi-cloud deployment and will implement backend-as-a service architecture.

Color quantization is the process of reducing number of colors used in an image while trying to maintain the visual appearance of the original image. In general, it is a form of cluster analysis, if each RGB color value is considered as a coordinate triple in the 3D colorspace.

K-mean clustering

Implementations of Machine Learning algorithms in Python

Sample Python API using flask, uses PyTorch to cluster image vectors

CSE 601 Data mining and bioinformatics

This technique reduces the number of distinct colors in an image, to produce a visually similar but compressed image.It uses K-Means Clustering to group pixels of similar color. The K centroids of the clusters represent 3D RGB color space & would replace the colors of all points in their cluster resulting in the image with K colors.

A collection of scripts by me.

K-means Clustering in Python without using any libraries

K-Means++ Clustering using Gap Statistic for determining optimal value of K in Python

Library and hand-made clustering algorithms are implemented in this project

K-Means Image Compression is a Python-based project that compresses an image by reducing the number of colors used. This technique is implemented using the K-Means clustering algorithm, making it ideal for those looking to understand and apply machine learning concepts in image processing.

Machine Learning Projects