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BEGIN:VEVENT
DTSTAMP:20260619T072752Z
UID:7b15a3c8-b235-4621-8d35-a88626572ae1
DTSTART:20201008T180000Z
DTEND:20201008T190000Z
DESCRIPTION:Educators:\nTDA\n \nDate:\nThursday October 8\, 2020\n6 PM - 7 
 PM UTC +2 (Berlin)\n \nLocation:\nOnline\, https://www.knime.com/about/eve
 nts/integrated-deployment-in-action-end-to-end-data-science-for-bioactivit
 y-prediction-oct-8-2020\n \nContents:\nDuring this webinar\, we will guide
  you through the complete journey of a data scientist: from training and s
 electing the best machine learning model for your data to putting your mod
 el into production and creating a simple web application.\nFor this\, we w
 ill demonstrate a use case of bioactivity prediction.\nWe will:\n• Train
  and optimize four different machine learning methods (Naive Bayes\, Logis
 tic Regression\, Random Forest\, XGBoost)\n• Identify the best model to 
 predict the activity of a compound on a particular biological target\n• 
 Use KNIME’s new integrated deployment functionality to automatically dep
 loy the best model\n• Create a simple web application that uses the depl
 oyed model to predict the activity of new compounds\nThe webinar will roun
 d off with a Q&amp\;A session. We look forward to lots of questions!\n \nL
 earning goals:\nUsing Machine Learning for bioactivity prediction\n \nPrer
 equisites:\nNone\n \nKeywords:\nCheminformatics\, Bioactivity Prediction\,
  KNIME\n \nTools:\nKNIME\n \nContact:\nAlexander Fillbrunn\nalexander.fill
 brunn@uni-konstanz.de
SUMMARY:Integrated Deployment in Action: End to End Data Science for Bioact
 ivity Prediction 
URL;VALUE=URI:https://www.denbi.de/training/984-integrated-deployment-in-ac
 tion-end-to-end-data-science-for-bioactivity-prediction
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