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scikit-learn day

13 Juin 2017 — scikit-learn day

09h-17h

ESILV, Paris Big Data

Résumé

The scikit-learn days will unite a community of users and developers doing machine learning and data science in Python. The goals are to provide Python enthusiasts a place to share ideas and learn from each other about how best to apply the language and tools to ever-evolving challenges in the vast realm of data management, processing, analytics, and visualization.

We aim to be an accessible, community-driven conference, with tutorials for novices, advanced topical workshops for practitioners, andopportunities for package developers and users to meet in person.

Our goal is to provide a discussion forum across all the various domains of data analysis to share experiences and techniques on data as well as progresses of libraries.

Direction de programme

Programme dirigé par Alexandre Gramfort (Telecom ParisTech) et Gaël Varoquaux (Inria)

Description détaillée

The Scikit-Learn Day is organised by the Scikit-Learn core development team, as part of PyParis2017.

Easy access from Paris (RER A - La Défense)

Programme résumé

  • 09:50: Machine Learning for Computer Security Experts using Python & scikit-learn

    Anaël Bonneton, ANSSI - ENS Paris

  • 10:30: Cloud computing made easy in Joblib

    Alexandre Abadie, Inria

  • 10:50: Imbalanced learn

    Guillaume Lemaître, Inria Saclay

  • 11:10: Introduction to pomegranate

    Venkat Raghav Rajagopalan, Telecom Paristech

  • 14:00: Building and deploying a predictive API using scikit-learn, Flask and Docker

    Nawfal Tachfine, Aramisauto.com

  • 14:40: How to prepare your text data for NLP applications

    Loryfel Nunez, FindSignal

  • 15:20: Machine Learning to moderate ads in real world classified’s business

    Vaibhav Singh, OLX Naspers Services GmbH

Lieu

ESILV

Ville: Paris


Programme détaillé

  • 09:50 - Machine Learning for Computer Security Experts using Python & scikit-learn

    Anaël Bonneton, ANSSI - ENS Paris

    We present SecuML, a Python open source tool that aims to foster the use of Machine Learning in Computer Security. It allows security experts to train models easily and comes up with a user interface to visualize the results and interact with the models.

  • 10:30 - Cloud computing made easy in Joblib

    Alexandre Abadie, Inria

    Joblib is a Python package initially designed for efficient computing of embarrassingly parallel problems on a local computer or a laptop. This talk gives a short introduction of the features provided by Joblib and the recent developments that make them usable on Cloud computing infrastructures.

  • 10:50 - Imbalanced learn

    Guillaume Lemaître, Inria Saclay

    Imbalanced-learn, a Python module to perform under sampling and over sampling with various techniques.

  • 11:10 - Introduction to pomegranate

    Venkat Raghav Rajagopalan, Telecom Paristech

    Pomegranate is a python module for probabilistic modelling focusing on both ease of use and speed, beating out competitors in benchmarks. In this talk I will describe how to use pomegranate to simply create sophisticated hidden Markov models, Bayesian Networks, General Mixture Models (and more!).

  • 14:00 - Building and deploying a predictive API using scikit-learn, Flask and Docker

    Nawfal Tachfine, Aramisauto.com

    The aim of this workshop is to expose a trained scikit-learn machine learning model as a REST API, built with Flask and Docker, to be queried by any system in JSON.

  • 14:40 - How to prepare your text data for NLP applications

    Loryfel Nunez, FindSignal

    Data cleaning is not as sexy as the the actual NLP algorithms. True. BUT, how your prepare your data will determine how well, or poorly your algorithm will perform. This talk will focus on Python’s libraries to extract important text or to remove unwanted text to prepare your data for NLP tasks.

  • 15:20 - Machine Learning to moderate ads in real world classified’s business

    Vaibhav Singh, OLX Naspers Services GmbH

    In this talk we share our experiences on how we at OLX Berlin built machine learning models to moderate 100+ million classified ads every month. Audience will get a chance to experience a real world of content moderation and a race to beat online fraudsters and scammers.

Organisateurs et/ou sponsors

Organisateurs (2017)

Co-organisateurs et sponsors (2017)

Ils parlent de l'OSIS (2017)