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Machine Learning 102: Regression

Machine Learning 102: Regression

Have you always been curious about what machine learning can do for your business problem, but could never find the time to learn the practical necessary skills? Do you wish to learn what Classification, Regression, Clustering and Feature Extraction techniques do, and how to apply them using the Oracle Machine Learning family of products?

Join us for this special series “Oracle Machine Learning Office Hours – Machine Learning 101”, where we will go through the main steps of solving a Business Problem from beginning to end, using the different components available in Oracle Machine Learning: programming languages and interfaces, including Notebooks with SQL, UI, and languages like R and Python.

In this fourth session in the series we covered Regression 102, with a look at multiple input attributes, attribute selection, feature generation, and a deeper look into diagnosis and potential problems.

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Marcos Arancibia

August 04, 2020
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  1. With Marcos Arancibia, Product Manager, Data Science and Big Data

    @MarcosArancibia Mark Hornick, Senior Director, Product Management, Data Science and Machine Learning @MarkHornick oracle.com/machine-learning Oracle Machine Learning Office Hours Machine Learning 102 – Regression Copyright © 2020, Oracle and/or its affiliates. All rights reserved
  2. Today’s Agenda Upcoming session Speaker Marcos Arancibia – Machine Learning

    101 Q&A Copyright © 2020 Oracle and/or its affiliates.
  3. Next Session August 25, 2020: Oracle Machine Office Hours, 8AM

    US Pacific Machine Learning 101 – Clustering Have you always been curious about what machine learning can do for your business problem, but could never find the time to learn the practical necessary skills? Do you wish to learn what Classification, Regression, Clustering and Feature Extraction techniques do, and how to apply them using the Oracle Machine Learning family of products? Join us for this special series “Oracle Machine Learning Office Hours – Machine Learning 101”, where we will go through the main steps of solving a Business Problem from beginning to end, using the different components available in Oracle Machine Learning: programming languages and interfaces, including Notebooks with SQL, UI, and languages like R and Python. This fourth session in the series will cover Clustering 101, where we will learn the terminology around Clustering or Segmentation, how to get the data prepared for clustering, how to measure cluster separation, identify potential pitfalls and see use cases. Copyright © 2020, Oracle and/or its affiliates. All rights reserved
  4. For product info… https://www.oracle.com/machine-learning Copyright © 2020 Oracle and/or its

    affiliates.
  5. Copyright © 2020 Oracle and/or its affiliates. https://www.oracle.com/cloud/free/

  6. Today’s Session: Machine Learning 101 - Regression In this fourth

    session in the Machine Learning 101 Series we will cover Regression 102, with a look at multiple input attributes, attribute selection, feature generation, and a deeper look into diagnosis and potential data problems. We will continue to make use of Oracle Machine Learning Notebooks, with Python and SQL as the underlying languages and OML4Py with AutoML for our demo environment. Copyright © 2020, Oracle and/or its affiliates. All rights reserved
  7. Copyright © 2020 Oracle and/or its affiliates 7 Regression 102

    • Data Prep for multiple attributes • Data Analysis • Model Build and Diagnostics • AutoML • Model Explainability • Conclusions Regression 101 • What is machine learning? • What is Regression? • History of Regression • Types of data needed for Regression • Terminology • Data Preparation • Linear Regression Intuition • Other Regression Algorithms • Model evaluation • AutoML • Q&A Agenda
  8. Copyright © 2020, Oracle and/or its affiliates 8 Demo on

    OML4Py, Regression on multiple attributes, Data Prep, Data Analysis, Model Build, AutoML and Model Explainability
  9. For more information… oracle.com/machine-learning Copyright © 2020 Oracle and/or its

    affiliates.
  10. Copyright © 2020, Oracle and/or its affiliates 10 Q &

    A
  11. Thank You Marcos Arancibia | marcos.arancibia@oracle.com Mark Hornick | mark.hornick@oracle.com

    Oracle Machine Learning Product Management