KNIME Data Science Learnathon: From Raw Data To Deployment - Paris - November 2018

You’re currently using the open source KNIME Analytics Platform, but looking for more functionality - especially for working across teams and business units? KNIME Server is the enterprise software for team based collaboration, automation, management, and deployment of data science workflows, data, and guided analytics. Non experts are given access to data science via KNIME Server WebPortal or can use REST APIs to integrate workflows as analytic services to applications, IoT, and systems. These slides are from a webinar where we introduce all KNIME Server features. It covers everything you need to manage your analytics at scale - deploying your workflows for sharing and collaboration, scheduling and automating tasks, templating and version control, as well as enterprise integration. it highlights the power of the REST API of KNIME Server, and the KNIME WebPortal - the ideal way for bringing data analytics to your non experts. View the webinar at: https://www.youtube.com/watch?v=qFV4P9-enZk

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From Raw Data to Deployment

The presentation summarizes most of the hands on materials from a course that I've been giving through the past few weeks, on basic machine learning techniques with implementation using KNIME . I hope these materials would become useful for other learners who are making their first steps with this great platform The course covers: * basic I/O * classification * regression * prediction * evaluation * feature selection * hyper-parameter optimization * basic feature extraction * deep learning basics for KNIME analytics platform ** Note that the live course also included additional theoretical lectures and materials that formed a basic understanding of the underlying principles behind the described hands-on content

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Slides from our recent webinar highlighting the newest features in KNIME Analytics Platform 4.1 and KNIME Server 4.10 It covers all the new features like Guided Labeling and all the new nodes such as the Binary Classification Inspector node, and WebRetriever node. It covers public and private spaces on the KNIME Hub and how the Hub can help you build your workflows more quickly and easily by giving you access to components. It also covers the additional cloud connectivity as well as the new Create Databricks Environment node for connecting to your Databricks cluster running on Microsoft Azure or Amazon AWS. On the KNIME Server side, we highlight how the server now supports the open standard for authorization - OAuth identification as well as how you can more easily configure workflows that are already running on KNIME Server. View the webinar here: https://www.youtube.com/watch?v=VzNqE4WklEk Read here for more details on this release: https://www.knime.com/whats-new-in-knime-41

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This presentation shows how to deal with the problem of fraud detection with 1. Classic machine learning techniques. All supervised machine learning algorithms for classification will do, e.g. Random Forest, Logistic Regression, etc. 2. Techniques from the outlier detection or the anomaly detection approach, e.g. autoencoder and isolation forest First presented by Maarit Widmann (KNIME) at "Data Talks: Handling Customer Data" Meetup in Berlin in January 2020.

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This presentation shows how to apply advanced analytics to forecast the need for timely restocking of bike stations in Washington DC, by taking into account past history, weather, calendar, traffic conditions, and additional external information. A model is created to predict the restocking need of each station by the hour and based on the minimum bare number of input parameters. The minimum bare number of parameters, necessary to guarantee an adequate prediction accuracy turned out to be: bike station, current bike ratio at the station (# bikes/# slots), time of the day, and weekend vs. business day.

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This presentation shows techniques for inference out of financial data based on the example of credit card fraud detection. Depending on whether the data at hand are labeled or not, one of the following techniques can be used: 1. Classic machine learning techniques. All supervised machine learning algorithms for classification will do, e.g. Random Forest, Logistic Regression, etc. 2. Techniques from the outlier detection or the anomaly detection approach, e.g. autoencoder and isolation forest First presented by Maarit Widmann and Mathilde Humeau (KNIME) at the "Kundenorientierte Innovation in Financial Services" event in Zurich in January 2020 by Sandsiv and Google.

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