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

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DURCHFÜHRUNG MIT TERMIN
Nr.
30261

Dauer
1 Tag ( 7 Stunden)

Preis
800,00 € netto
952,00 € inkl. 19% MwSt.
TERMIN UND ORT NACH ABSPRACHE
Nr.
30261

Dauer
1 Tag ( 7 Stunden)


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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.

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 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

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

Weitere Informationen

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