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Test de tout
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linogaliana committed Nov 30, 2023
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2 changes: 1 addition & 1 deletion content/NLP/02_exoclean.qmd
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plus approfondie les différentes méthodes présentées
précédemment.
bibliography: ../../reference.bib
image: featured_nlp_exo.png
image: https://minio.lab.sspcloud.fr/lgaliana/generative-art/pythonds/featured_nlp_exo.png
echo: false
---

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connu sous son acronyme anglais : NLP pour _natural langage processing_).
Cette partie vise à illustrer la richesse de cet écosystème à partir
de quelques exemples littéraires : Dumas, Poe, Shelley, Lovecraft.
image: nlp.png
image: https://minio.lab.sspcloud.fr/lgaliana/generative-art/pythonds/nlp.png
---

Les parties précédentes étaient consacrées à l'acquisition de compétences
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type: book
description: |
Un chapitre plus approfondi sur `ElasticSearch`
image: detective_map.png
image: https://minio.lab.sspcloud.fr/lgaliana/generative-art/pythonds/detective_map.png
---


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présente cette intégration d' `Elastic` avec l'exemple de la recherche
dans les données alimentaires de l' `OpenFoodFacts` Database
bibliography: ../../reference.bib
image: detective_words.png
image: https://minio.lab.sspcloud.fr/lgaliana/generative-art/pythonds/detective_words.png
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outils à l'état de l'art pour gagner en
efficacité ou améliorer la valorisation
d'un projet.
image: ferrari.png
image: https://minio.lab.sspcloud.fr/lgaliana/generative-art/pythonds/ferrari.png
---

Les parties précédentes étaient très tournées sur l'acquisition
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de nombreux avantages pour l'analyse de données (vitesse d'import, possibilité de traiter
des données plus volumineuses que la RAM...)
eval: false
image: python_cloud.png
image: https://minio.lab.sspcloud.fr/lgaliana/generative-art/pythonds/python_cloud.png
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::: {.cell .markdown}
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figés (`matplotlib`, `seaborn`, `geoplot`...) ou de
visualisation réactives (`plotly`, `folium`, etc.)
slug: visualisation
image: snake-chart.png
image: https://minio.lab.sspcloud.fr/lgaliana/generative-art/pythonds/snake-chart.png
bibliography: ../../reference.bib
---

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à travers un exercice permettant
de visualiser la fréquentation par les
vélos des routes parisiennes.
image: cartography.png
image: https://minio.lab.sspcloud.fr/lgaliana/generative-art/pythonds/cartography.png
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::: {.cell .markdown}
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les fonctionalités graphiques de `matplotlib`,
`seaborn` et `plotly` pour représenter des statistiques
sur les décomptes de vélo à Paris.
image: drawing.png
image: https://minio.lab.sspcloud.fr/lgaliana/generative-art/pythonds/drawing.png
echo: false
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