Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2)
- Kursuskode 0A079G
- Varighed 2 dage
Leveringsmetoder
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Leveringsmetoder
Kurset er tilgængeligt i følgende formater:
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Åbent kursus
Traditionel klasserumsundervisning
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Åbent kursus (Virtuelt)
Live klasserumsundervisning du tilgår virtuelt
Anmod om dette kursus Med en anden leveringsløsning
Beskrivelse
ToppenThis 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.
Virtuel deltagelse
Et V&C Select kursus indholder nøjagtig det samme som et almindeligt kursus. Før kursusstart modtager man kursusmaterialet. Dernæst logger man på kurset via internettet og ser via sin pc den selvsamme præsentation som de øvrige deltagere, man kommunikerer via chat med underviseren og de øvrige deltagere på kurset. Denne uddannelsesmodel er både tids-og omkostningsbesparende og kan være et oplagt alternativ til almindelig klasseundervisning, hvis man f.eks. har et begrænset rejsebudget.
Kursusdato
Toppen-
- Leveringsmetode: Åbent kursus (Virtuelt)
- Dato: 27-28 august, 2025
- Kursussted: Virtual
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- Leveringsmetode: Åbent kursus (Virtuelt)
- Dato: 12-13 november, 2025
- Kursussted: Virtual
Målgruppe
Toppen- Data scientists
- Business analysts
- Clients who want to learn about machine learning models
Kursets formål
ToppenAt the end of the course, participants will be able to :
- Use machine learning models
- Prepare data for modeling
Kursusindhold
Toppen- 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
Forudsætninger
Toppen- Knowledge of your business requirements
- Basic understanding of Data Science
Mere information
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