Rocket-Powered Data Science

JULY 6, 2021

Specifically, in the modern era of massive data collections and exploding content repositories, we can no longer simply rely on keyword searches to be sufficient. One type of implementation of a content strategy that is specific to data collections are data catalogs. Data catalogs are very useful and important.

Strategy 266

Strategy Machine Learning Metadata Knowledge Discovery 266

Data Science 101

OCTOBER 31, 2019

This data alone does not make any sense unless it’s identified to be related in some pattern. Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). Data Collection. Data Mining Models.

Data mining 77

Data mining KDD Data Science Forecasting 77

Ontotext

APRIL 4, 2019

Milena Yankova : We help the BBC and the Financial Times to model the knowledge available in various documents so they can manage it. This is extremely powerful, so literacy in data collection and data processing will be one of the crucial skills of the future. What exactly do you do for them?

Recreation/Entertainment 76

Recreation/Entertainment Testing Enterprise Knowledge Discovery 76

Ontotext

MAY 18, 2020

These are the so-called supercomputers, led by a smart legion of researchers and practitioners in the fields of data-driven knowledge discovery. Thanks to their might, now scientists and practitioners can develop innovative ways of collecting, storing, processing, and, ultimately, finding patterns in data.

Knowledge Discovery 72

Knowledge Discovery Experimentation Data-driven Metadata 72

Domino Data Lab

MAY 20, 2021

In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed. Insufficient training data in the minority class — In domains where data collection is expensive, a dataset containing 10,000 examples is typically considered to be fairly large.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

Domino Data Lab

AUGUST 1, 2021

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. The interest in interpretation of machine learning has been rapidly accelerating in the last decade. See Ribeiro et al.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139