Remove Data Mining Remove Natural Language Processing Remove Predictive Analytics
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Predictive analytics vs. AI: Why the difference matters in 2023?

Data Science Dojo

Artificial Intelligence (AI) and Predictive Analytics are revolutionizing the way engineers approach their work. This article explores the fascinating applications of AI and Predictive Analytics in the field of engineering. Descriptive analytics involves summarizing historical data to extract insights into past events.

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Fundamentals of Data Mining

Data Science 101

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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Was ist eine Vektor-Datenbank? Und warum spielt sie für AI eine so große Rolle?

Data Science Blog

Für Natural Language Processing ( NLP ) benötigen Modelle des Deep Learnings die zuvor genannten Word Embedding, also hochdimensionale Vektoren, die Informationen über Worte, Sätze oder Dokumente repräsentieren.

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Top 10 Machine Learning (ML) Tools for Developers in 2023

Towards AI

For instance, today’s machine learning tools are pushing the boundaries of natural language processing, allowing AI to comprehend complex patterns and languages. Scikit Learn Scikit Learn is a comprehensive machine learning tool designed for data mining and large-scale unstructured data analysis.

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How to tackle lack of data: an overview on transfer learning

Data Science Blog

My point is, the more data you have, and the bigger computation resource you have, the better performance you get. In other words, machine learning has scalability with data and parameters. This characteristic is clearly observed in models in natural language processing (NLP) and computer vision (CV) like in the graphs below.

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Elevating business decisions from gut feelings to data-driven excellence

Dataconomy

At its core, decision intelligence involves collecting and integrating relevant data from various sources, such as databases, text documents, and APIs. This data is then analyzed using statistical methods, machine learning algorithms, and data mining techniques to uncover meaningful patterns and relationships.

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Data science vs. machine learning: What’s the difference?

IBM Journey to AI blog

The fields have evolved such that to work as a data analyst who views, manages and accesses data, you need to know Structured Query Language (SQL) as well as math, statistics, data visualization (to present the results to stakeholders) and data mining.