Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2)
- Code training 0A079G
- Duur 2 dagen
Andere trainingsmethoden
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Methode
Deze training is in de volgende formats beschikbaar:
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Klassikale training
Klassikaal leren
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Virtueel leren
Virtueel leren
Vraag deze training aan in een andere lesvorm.
Trainingsbeschrijving
Naar bovenThis course provides an introduction to supervised models, unsupervised models, and association models. This is an application-oriented course and examples include predicting whether customers cancel their subscription, predicting property values, segment customers based on usage, and market basket analysis.
Virtueel en Klassikaal™
Virtueel en Klassikaal™ is een eenvoudig leerconcept en biedt een flexibele oplossing voor het volgen van een klassikale training. Met Virtueel en Klassikaal™ kunt u zelf beslissen of u een klassikale training virtueel (vanuit huis of kantoor )of fysiek op locatie wilt volgen. De keuze is aan u! Cursisten die virtueel deelnemen aan de training ontvangen voor aanvang van de training alle benodigde informatie om de training te kunnen volgen.
Data
Naar boven-
- Methode: Virtueel leren
- Datum: 27-28 augustus, 2025
- Locatie: Virtueel-en-klassikaal
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- Methode: Virtueel leren
- Datum: 12-13 november, 2025
- Locatie: Virtueel-en-klassikaal
Doelgroep
Naar boven- Data scientists
- Business analysts
- Clients who want to learn about machine learning models
Trainingsdoelstellingen
Naar bovenAt the end of the course, participants will be able to :
- Use machine learning models
- Prepare data for modeling
Inhoud training
Naar boven- Introduction to machine learning models
- Taxonomy of machine learning models
- Identify measurement levels
- Taxonomy of supervised models
- Build and apply models in IBM SPSS Modeler
Supervised models: Decision trees - CHAID
- CHAID basics for categorical targets
- Include categorical and continuous predictors
- CHAID basics for continuous targets
- Treatment of missing values
Supervised models: Decision trees - C&R Tree
- C&R Tree basics for categorical targets
- Include categorical and continuous predictors
- C&R Tree basics for continuous targets
- Treatment of missing values
- Evaluation measures for supervised models
- Evaluation measures for categorical targets
- Evaluation measures for continuous targets
Supervised models: Statistical models for continuous targets - Linear regression
- Linear regression basics
- Include categorical predictors
- Treatment of missing values
- Supervised models: Statistical models for categorical targets - Logistic regression
- Logistic regression basics
- Include categorical predictors
- Treatment of missing values
Association models: Sequence detection
- Sequence detection basics
- Treatment of missing values
Supervised models: Black box models - Neural networks
- Neural network basics
- Include categorical and continuous predictors
- Treatment of missing values
Supervised models:
- Black box models - Ensemble models
- Ensemble models basics
- Improve accuracy and generalizability by boosting and bagging
- Ensemble the best models
Unsupervised models: K-Means and Kohonen
- K-Means basics
- Include categorical inputs in K-Means
- Treatment of missing values in K-Means
- Kohonen networks basics
- Treatment of missing values in Kohonen
Unsupervised models: TwoStep and Anomaly detection
- TwoStep basics
- TwoStep assumptions
- Find the best segmentation model automatically
- Anomaly detection basics
- Treatment of missing values
Association models: Apriori
- Apriori basics
- Evaluation measures
- Treatment of missing values
Preparing data for modeling
- Examine the quality of the data
- Select important predictors
- Balance the data
Voorkennis
Naar boven- Knowledge of your business requirements
- Basic understanding of Data Science
Aanvullende informatie
Naar boven- #000000
