An approach to document exploration using Machine Learning. Let's cluster similar research articles together to make it easier for health professionals and researchers to find relevant research articles.

A computational toolkit in R for the integration, exploration, and analysis of high-dimensional single-cell cytometry and imaging data.

A fast and flexible framework for data reduction in R

A DataCamp course for the University of Helsinki

Analysis of Data Scientist Job Descriptions using Natural Language Processing

Visually explore a game of semantle using a UMAP representation of the underlying word2vec word embedding.

Here we present a way to cluster images using Keras (VGG16), UMAP & HDBSCAN

Creating Customer Segments - 4th project for Udacity's Machine Learning Nanodegree

To use deep learning to identify patients whose tumor DNA mutations “look similar to” other tumors for which treatments are effective.

Using machine learning models to predict if patients have chronic kidney disease based on a few features. The results of the models are also interpreted to make it more understandable to health practitioners.

PCA and Gaussian Mixture Model Clustering to uncover hidden segments in customer purchase data.

Analysis on hospital readmission data for diabetic patients using R and Tableau

Classifying users on social media, using text embeddings from OpenAI and others

Predicting Nobel Physics Prize winners. Final project for Harvard CS109a 2017 edition https://github.com/covuworie/a-2017.

Artist Similarity Mapping

Data Exploration, and Analysis of accidents in the UK using Unsupervised ML algorithms

Principal Component Analysis is One of the Most Popular Dimensionality Reduction Algorithms used in Machine Learning Which comes under Unsupervised Way of Learning. It is also Used as a way of Feature Extraction where, More Information is Extracted from all the Existing Attributes, in just some 3-4 Attributes using the Concepts of Eigen Values a…

Analyze customers using unsupervised learning, PCA and K-Mean Clustering of Arvato dataset

Applied unsupervised learning techniques on demographic and spending data for a sample of German households.