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Five (5) Oracle Machine Learning Notebooks You Should Know!
Oracle Machine Learning Notebooks provide a collaborative user interface for data scientists and business and data analysts who perform machine learning in Oracle Autonomous Database–both Autonomous Data Warehouse (ADW) and Autonomous Transaction Processing (ATP). Oracle Machine Learning Notebooks enables data scientists, citizen data scientists, and data analysts to work together to explore their data visually and develop analytical methodologies. The Notebooks interface provides access to Oracle’s high performance, parallel and scalable in-database implementations of machine learning algorithms via SQL and PL/SQL, with support for Python and R coming soon. Easy to understand and easy to repurpose, OML notebooks enable novice, intermediate and expert Oracle data professionals to leverage OML’s 30+ algorithms for classification, regression, anomaly detection, attribute importance, clustering, associations, text mining and other machine learning problems. This HOL session steps users through five (5) “must see” OML notebooks that serve as the core of many data science projects: classification, attribute importance, time-series forecasting, text mining and anomaly detection. Come learn how to get started using Oracle Machine Learning to tackle a wide variety of data-driven problems.
This session was orginally included in the UKOUG Conference 2020 however, due to a technical issue it did not run as planned.
Register
This event is free for members of the UKOUG. If you are a guest that registered for the UKOUG Conference 2020, please <a>contact us</a> with your booking details to book for this session for free.
Speaker
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Charlie Berger, Oracle
