Smart Data Collective
MARCH 23, 2022
Well, if you are someone who has loads of data and aren’t using it for your surveys and you would love to learn more on how to use it, don’t go anywhere because, in this article, we will show you data mining tips you can use to leverage your surveys. 5 data mining tips for leveraging your surveys.
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). Machine learning provides the technical basis for data mining.
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Jet Global
JUNE 5, 2019
So how does a leading-edge business find a way to marry their wealth of data with the opportunity to utilize it effectively via BI software? Let’s introduce the concept of data mining. Toiling Away in the Data Mines. Clustering helps to group data and recognize differences and similarities.
O'Reilly on Data
JANUARY 22, 2019
An exploration of three types of errors inherent in all financial models. At Hedged Capital , an AI-first financial trading and advisory firm, we use probabilistic models to trade the financial markets. All financial models are wrong. Clearly, a map will not be able to capture the richness of the terrain it models.
CIO Business Intelligence
OCTOBER 13, 2023
Solutions data architect: These individuals design and implement data solutions for specific business needs, including data warehouses, data marts, and data lakes. Application data architect: The application data architect designs and implements data models for specific software applications.
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.
Rocket-Powered Data Science
JULY 7, 2019
Autonomous Vehicles: Self-driving (guided without a human), informed by data streaming from many sensors (cameras, radar, LIDAR), and makes decisions and actions based on computer vision algorithms (ML and AI models for people, things, traffic signs,…). Examples: Cars, Trucks, Taxis. They cannot process language inputs generally.
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