Data Science 101

OCTOBER 31, 2019

Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). The models created using these algorithms could be evaluated against appropriate metrics to verify the model’s credibility. Data Mining Models. Classification.

Data mining 77

Data mining KDD Data Science Forecasting 77

Ontotext

MAY 18, 2020

Ever since Hippocrates founded his school of medicine in ancient Greece some 2,500 years ago, writes Hannah Fry in her book Hello World: Being Human in the Age of Algorithms , what has been fundamental to healthcare (as she calls it “the fight to keep us healthy”) was observation, experimentation and the analysis of data. Certainly not!

Knowledge Discovery 72

Knowledge Discovery Experimentation Data-driven Metadata 72

The Unofficial Google Data Science Blog

JANUARY 14, 2016

There are commercial sites which allow users to search for and purchase goods or book rooms they desire. Of particular interest to LSOS data scientists are modeling and prediction techniques which keep improving with more data. A consequence of the LSOS business model? They also tend to care about small effect fractions.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Ontotext

JULY 29, 2021

One of its pillars are ontologies that represent explicit formal conceptual models, used to describe semantically both unstructured content and databases. We rather see it as a new paradigm that is revolutionizing enterprise data integration and knowledge discovery. We can’t imagine looking at the Semantic Web as an artifact.

Enterprise 124

Enterprise Metadata Knowledge Discovery Management 124

Ontotext

AUGUST 10, 2023

Their interoperability and the supported network standards for communication enable devices to seamlessly connect and interact regardless of make, model, or operating system. They make this possible by adding domain knowledge that puts your organization’s data in context and enables its interpretation.

Technology 52

Technology Knowledge Discovery Interactive Enterprise 52

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

But most common machine learning methods don’t give posteriors, and many don’t have explicit probability models. More precisely, our model is that $theta$ is drawn from a prior that depends on $t$, then $y$ comes from some known parametric family $f_theta$. Here, our items are query-ad pairs. Calculate posterior quantities of interest.

KDD 40

KDD Testing Machine Learning Modeling 40