Dataconomy

SEPTEMBER 22, 2023

Artificial intelligence (AI) can be used to automate and optimize the data archiving process. There are several ways to use AI for data archiving. This process can help organizations identify which data should be archived and how it should be categorized, making it easier to search, retrieve, and manage the data.

Clustering 185

Clustering Algorithm Data Classification Machine Learning 185

IBM Journey to AI blog

DECEMBER 20, 2023

Classification algorithms —predict categorical output variables (e.g., “junk” or “not junk”) by labeling pieces of input data. Classification algorithms include logistic regression, k-nearest neighbors and support vector machines (SVMs), among others.

Machine Learning 75

Machine Learning Machine Learning Supervised Learning Clustering 75

AWS Machine Learning Blog

NOVEMBER 1, 2023

Organizations can search for PII using methods such as keyword searches, pattern matching, data loss prevention tools, machine learning (ML), metadata analysis, data classification software, optical character recognition (OCR), document fingerprinting, and encryption.

AWS 83

AWS Machine Learning Machine Learning Clustering 83

Smart Data Collective

MAY 16, 2022

Data models help in storing and retrieving the data efficiently. Data mining: is the process of discovering patterns in the data by applying different techniques such as data classification, clustering, regression, association, time series prediction, etc.

Data Warehouse 127

Data Warehouse Data Mining Data Mining Data Mining 127

Towards AI

MAY 3, 2023

This code can cover a diverse array of tasks, such as creating a KMeans cluster, in which users input their data and ask ChatGPT to generate the relevant code. In the realm of data science, seasoned professionals often carry out research to comprehend how similar issues have been tackled in the past.

ML 80

ML ML Machine Learning Machine Learning 80

Dataconomy

AUGUST 31, 2023

The goal of unsupervised learning is to identify structures in the data, such as clusters, dimensions, or anomalies, without prior knowledge of the expected output. This can be useful for discovering hidden patterns, identifying outliers, and reducing the complexity of high-dimensional data.

Machine Learning 136

Machine Learning Machine Learning Supervised Learning Algorithm 136