Machine Learning for the rest of us

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MACHINE LEARNING for the rest of us @providenz

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$ whoami @providenz

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datamateur $ whoami

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Machine learning ? Pieces Chambres Surface Quartier Prix 3 2 90 Montferrand 137000 3 3 76 La Plaine 168500 7 5 160 Les Cézeaux 276500 6 4 126 Saint Alyre 149000 … … … … …

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Machine learning ? Pieces Chambres Surface Quartier Prix 8 4 101 Pré de la reine ??????

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Machine learning if block == sthg: if area < 90: if rooms > 3: if bedrooms >= 2: sq_meters_price = XX if …

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Machine learning ? phase 1/2: apprentissage ALGO Moult voire + MODÈLE Données

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Machine learning ? phase 2/2: prédiction Prédiction MODÈLE Pieces Chambres Surface Quartier Prix 8 4 101 Pré de la reine ??????

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Applications Vision Moteurs de recherche Antispam Systèmes de recommandations Finance Détection de fraude Médical Analyse de sentiment Détection de langue Shazam Siri Art …

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2 types de problèmes Classiﬁcation Régression

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Classiﬁcation

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Iris Setosa Versicolor Virginica

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Régression

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Implémentations

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En python scikit-learn gensim orange …

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Hors python Weka: http://www.cs.waikato.ac.nz/ml/weka/ Vowpal wabbit: https://github.com/JohnLangford/ vowpal_wabbit Shogun: http://www.shogun-toolbox.org/

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Predictives apis BIGML : bigml.com GOOGLE PREDICTION API aka ML as a service

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Python

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Process Spéciﬁer le problème Collecter et préparer les données Feature engineering Sélectionner un algo Training Validation Prédiction

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Feature engineering Pieces Chambres Surface Lat Long Prix 3 2 90 45.77966 3.08628 137000 Pieces Chambres Surface Quartier Prix 3 2 90 137000

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Pieces Chambres Surface Quartier 3 2 90 Montferrand 3 3 76 La Plaine 7 5 160 Les Cézeaux 6 4 126 Saint Alyre Prix 137000 168500 276500 149000 Features Target 3 2 68 N/A 3 2 67 Gare 93000 112000 Train Test Cross validation

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SCIKIT LEARN http://scikit-learn.org/ pip install scikit-learn

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Compléments ipython pandas numpy scipy hdf5 nltk

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Classiﬁers (Algos) random forest k-means support vector machines lasso ridge regression …

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Notebooks

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Warnings

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Trop peu de données

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Warnings Corrélation != rapport de causes à effets Spurious correlations http://tylervigen.com/

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Ressources http://scikit-learn.org/ http://pandas.pydata.org/ http://matplotlib.org/ http://stanford.edu/~mwaskom/software/seaborn/ https://www.kaggle.com/ libs challenges

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Ressources: livres Bootstrapping machine learning: http:// www.louisdorard.com/machine-learning-book/ Building Machine Learning Systems with Python: https:// www.packtpub.com/big-data-and-business-intelligence/ building-machine-learning-systems-python Programming collective intelligence : http:// www.amazon.com/gp/product/0596529325/ An Introduction to Statistical Learning: http://www- bcf.usc.edu/~gareth/ISL/ La statistique sans formule mathématique Bernard Py

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https://www.ﬂickr.com/photos/52569650@N00/ https://www.ﬂickr.com/photos/haglundc/ Crédits photo

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Merci @providenz