IBM 0A039G - Advanced Machine Learning Models Using IBM SPSS Modeler (V18.2) - Seminar / Kurs von PROKODA GmbH

Inhalte

Course Outline

Introduction to advanced machine learning models· Taxonomy of models· Overview of supervised models· Overview of models to create natural groupingsGroup fields: Factor Analysis and Principal Component Analysis· Factor Analysis basics· Principal Components basics· Assumptions of Factor Analysis· Key issues in Factor Analysis· Improve the interpretability· Factor and component scoresPredict targets with Nearest Neighbor Analysis· Nearest Neighbor Analysis basics· Key issues in Nearest Neighbor Analysis· Assess model fitExplore advanced supervised models· Support Vector Machines basics· Random Trees basics· XGBoost basicsIntroduction to Generalized Linear Models· Generalized Linear Models· Available distributions· Available link functionsCombine supervised models· Combine models with the Ensemble node· Identify ensemble methods for categorical targets· Identify ensemble methods for flag targets· Identify ensemble methods for continuous targets· Meta-level modelingUse external machine learning models· IBM SPSS Modeler Extension nodes· Use external machine learning programs in IBM SPSS ModelerAnalyze text data· Text Mining and Data Science· Text Mining applications· Modeling with text data

Objective

Introduction to advanced machine learning models · Taxonomy of models · Overview of supervised models · Overview of models to create natural groupings

Group fields: Factor Analysis and Principal Component Analysis · Factor Analysis basics · Principal Components basics · Assumptions of Factor Analysis · Key issues in Factor Analysis · Improve the interpretability · Factor and component scores

Predict targets with Nearest Neighbor Analysis · Nearest Neighbor Analysis basics · Key issues in Nearest Neighbor Analysis · Assess model fit

Explore advanced supervised models · Support Vector Machines basics · Random Trees basics · XGBoost basics

Introduction to Generalized Linear Models · Generalized Linear Models · Available distributions · Available link functions

Combine supervised models · Combine models with the Ensemble node · Identify ensemble methods for categorical targets · Identify ensemble methods for flag targets · Identify ensemble methods for continuous targets · Meta-level modeling

Use external machine learning models · IBM SPSS Modeler Extension nodes · Use external machine learning programs in IBM SPSS Modeler

Analyze text data · Text Mining and Data Science · Text Mining applications · Modeling with text data

Hinweise

Unterrichtsmethode

presentation, discussion, hands-on exercises

Dieses Training bieten wir in Kooperation mit der Integrata AG an.

Termine und Orte

Datum Uhrzeit Dauer Preis
Hamburg, DE
25.11.2020 09:00 - 16:00 Uhr 8 h Jetzt buchen ›
München, DE
25.11.2020 09:00 - 16:00 Uhr 8 h Jetzt buchen ›

SG-Seminar-Nr.: 5505831

Termine

  • 25.11.2020

    München, DE

    Hamburg, DE

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Datum Uhrzeit Dauer Preis
Hamburg, DE
25.11.2020 09:00 - 16:00 Uhr 8 h Jetzt buchen ›
München, DE
25.11.2020 09:00 - 16:00 Uhr 8 h Jetzt buchen ›