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# System prepended metadata

title: Ressources Utiles

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# Ressources Utiles

## Sites

Quelque site pour apprendre, questionner et se former.

* les moteurs de recherche: Google, Duckduckgo, Bing, Qwant
* wiki: [Wikipedia](wikipedia.com)
* articles scientifiques: [DBLP](https://dblp.uni-trier.de/), [Google Schoolar](https://scholar.google.com/)
* Questions/Réponses: [Stack Overflow](https://stackoverflow.com/), [Stack Exchange](https://stackexchange.com/sites#) (notamment [crossvalidated](https://stats.stackexchange.com/) pour la science des données.)
* Communité:  article dev, tutoriels, forum idées:
    * général: [Medium](https://medium.com/), [Dev.to](https://dev.to/)
    * spécialisé : [Toward Data Science](]https://towardsdatascience.com/), [Data science Central](https://www.datasciencecentral.com)
* blogs...
* les plateforme de code libre: [Github](https://github.com) et [Gitlab](https://gitlab.com)
* les awesome: Awesome ressources about everithing: https://github.com/sindresorhus/awesome
* cheatsheet (antiséche):
    * language de programmation:  https://devhints.io/
    * cheatsheet for data science:
        * [Good one](https://st2.ning.com/topology/rest/1.0/file/get/1211570060?profile=original)
        * [Kaggle cheatsheet](https://www.kaggle.com/timoboz/data-science-cheat-sheets)
        * [Kdnuggets cheatsheet](https://www.kdnuggets.com/2018/09/meverick-lin-data-science-cheat-sheet.html)
        * [Data viz](https://www.kdnuggets.com/2018/08/data-visualization-cheatsheet.html)
        * [Data Camp](https://www.datacamp.com/community/data-science-cheatsheets)

## Librairies

Quelques librairies python incontournable.

* Machine Learning: [scikit-learn](scikit-learn.org/)
* Data viz: [matplotlib](https://matplotlib.org/), plotly, Dash
* Efficient math and Computing): [numpy](https://numpy.org/), [scipy](https://www.scipy.org/), +[scipy lectures](http://scipy-lectures.org/)
* Deep learning: Kera, Pytorch, TensorFlow.
* Natural Langage Processing (NLP): nltk and spaCy

## Livres

* Pattern Recognition and Machine Learning (Bishop): Pour appronfir et comprendre les mathématiques derrière les algorithmes de ML: [here](http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf)
* Ihtroduction to Machine Learning with Python: [here](https://ipfs.io/ipfs/bafykbzacecewkthyv3ml3ja7bcyhaohbunvzyqcmx7gaef3tg2lcxrvov5ita?filename=Andreas%20C.%20M%C3%BCller%2C%20Sarah%20Guido%20-%20Introduction%20to%20Machine%20Learning%20with%20Python_%20A%20Guide%20for%20Data%20Scientists-O%E2%80%99Reilly%20Media%20%282016%29.pdf)
* Learning scikit-learn: [here](ttps://ipfs.io/ipfs/bafykbzaceavddc6g4pln5qekz4czxu4sqz4z5us56ltsrjmwuhupoqk3mofui?filename=Ra%C3%BAl%20Garreta%2C%20Guillermo%20Moncecchi%20-%20Learning%20scikit-learn_%20Machine%20Learning%20in%20Python-Packt%20%282013%29.pdf)





