Courbevoie OSIS 2017 - Scikit-learn day
On Tuesday 13 June 2017 from 09h30 to 17h30.
ESILV, 12 avenue Léonard de Vinci, Courbevoie, Île-de-France
The scikit-learn days will unite a comunity 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.
- 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.
Cette journée fait partie du Printemps de l'innovation Open Source, rendez-vous de l’excellence scientifique et technologique du libre et de l’open source. Initiée par le GTLL (Groupe thématique Logiciel libre de Systematic) et l’Irill (Initiative de Recherche et Innovation sur le Logiciel libre), l'OSIS vise à montrer le rayonnement international de projets issus de la recherche et de l’innovation française.
- scikit-learn gtll printemps-de-l-innovation-open-source osis osis-2017 open-source