Text mining: Definition, techniques, use cases
Text mining consists in using Machine Learning for text analysis. Discover all you need to know: definition, functioning, techniques, advantages, use cases… Modern companies have
🚀 Think you’ve got what it takes for a career in Data? Find out in just one minute!
Text mining consists in using Machine Learning for text analysis. Discover all you need to know: definition, functioning, techniques, advantages, use cases… Modern companies have
U-NET is a neural network model dedicated to Computer Vision tasks and more particularly to Semantic Segmentation problems. Discover all you need to know: presentation,
Still unknown, the job of Machine Learning Engineer is more and more sought after in companies. Find out everything you need to know about this
Elasticsearch is a distributed open-source data search and analysis engine based on Apache Lucene and developed in Java. The project began as a scalable version
The development of Big Data has pushed the creation of a wide variety of tools that facilitate the task of highlighting and studying the value
Data cleaning is an essential step in Data Science and Machine Learning. It consists in solving problems in data sets, to be able to exploit
In the era of Big Data, companies are collecting ever larger amounts of data. But not all companies are making the most of it. And
The GitHub platform allows computer programmers to freely collaborate on code projects. Find out everything you need to know about this massively used service in
Hadoop is an Open Source framework dedicated to Big Data storage and processing. Discover everything you need to know: definition, history, functioning, advantages, training… For
The Python language being one of the most used, it contains a lot of frameworks, and many of them are developed exclusively for Data Science.
The Deep Neural Network imitates the functioning of the human brain. Find out everything you need to know about it: definition, functioning, use cases, training.A
This article will be divided into two parts: The first focuses on the choice of metrics specific to this type of data, the second details