Schulung - IBM 0A039G - Advanced Machine Learning Models Using IBM SPSS Modeler (V18.2)

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DURCHFÜHRUNG MIT TERMIN
Nr.
30261

Dauer
8h00

Preis
800,00 € netto
952,00 € inkl. 19% MwSt.

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Overview

This course presents advanced models available in IBM SPSS Modeler. The participant is first introduced to a technique named PCA/Factor, to reduce the number of fields to a number of core factors, referred to as components or factors. The next topics focus on supervised models, including Support Vector Machines, Random Trees, and XGBoost. Methods are reviewed on how to analyze text data, combine individual models into a single model, and how to enhance the power of IBM SPSS Modeler by adding external models, developed in Python or R, to the Modeling palette.

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Zielgruppe

Wer sollte teilnehmen:

Zielgruppe

Audience

  • Data scientists
  • Business analysts
  • Experienced users of IBM SPSS Modeler who want to learn about advanced techniques in the software

Voraussetzungen

Prerequisites

  • Knowledge of your business requirements
  • Required: IBM SPSS Modeler Foundations (V18.2) course (0A069G/0E069G) or equivalent knowledge of how to import, explore, and prepare data with IBM SPSS Modeler v18.2, and know the basics of modeling.
  • Recommended: Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2) course (0A079G/0E079G), or equivalent knowledge or experience with the product about supervised machine learning models (CHAID, C&R Tree, Regression, Random Trees, Neural Net, XGBoost), unsupervised machine learning models (TwoStep Cluster), and association machine learning models such as APriori.

Trainingsprogramm

Trainingsprogramm

Course Outline

Introduction to advanced machine learning modelsTaxonomy of modelsOverview of supervised modelsOverview of models to create natural groupingsGroup fields:  Factor Analysis and Principal Component AnalysisFactor Analysis basicsPrincipal Components basicsAssumptions of Factor AnalysisKey issues in Factor AnalysisImprove the interpretabilityFactor and component scoresPredict targets with Nearest Neighbor AnalysisNearest Neighbor Analysis basicsKey issues in Nearest Neighbor AnalysisAssess model fitExplore advanced supervised modelsSupport Vector Machines basicsRandom Trees basicsXGBoost basicsIntroduction to Generalized Linear ModelsGeneralized Linear ModelsAvailable distributionsAvailable link functionsCombine supervised modelsCombine models with the Ensemble nodeIdentify ensemble methods for categorical targetsIdentify ensemble methods for flag targetsIdentify ensemble methods for continuous targetsMeta-level modelingUse external machine learning modelsIBM SPSS Modeler Extension nodesUse external machine learning programs in IBM SPSS ModelerAnalyze text dataText Mining and Data ScienceText Mining applicationsModeling 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

Schulungsmethode

Schulungsmethode

presentation, discussion, hands-on exercises

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Weitere Informationen

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Schulung - IBM 0A039G - Advanced Machine Learning Models Using IBM SPSS Modeler (V18.2)