{"course":{"productid":20050,"modality":6,"active":true,"language":"en","title":"Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2)","productcode":"0A079G","vendorcode":"IB","vendorname":"IBM","fullproductcode":"IB-0A079G","courseware":{"has_ekit":true,"has_printkit":false,"language":"en"},"url":"https:\/\/portal.flane.ch\/course\/ibm-0a079g","essentials":"<ul>\n\n\t<li>Knowledge of your business requirements<\/li><\/ul>","audience":"<ul>\n\n\t<li>Data scientists<\/li><li>Business analysts<\/li><li>Clients who want to learn about machine learning models<\/li><\/ul>","contents":"<p>This 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.<\/p>","outline":"<p>Introduction to machine learning models&bull; Taxonomy of machine learning models&bull; Identify measurement levels&bull; Taxonomy of supervised models&bull; Build and apply models in IBM SPSS ModelerSupervised models: Decision trees - CHAID&bull; CHAID basics for categorical targets&bull; Include categorical and continuous predictors&bull; CHAID basics for continuous targets&bull; Treatment of missing valuesSupervised models: Decision trees - C&amp;R Tree&bull; C&amp;R Tree basics for categorical targets&bull; Include categorical and continuous predictors&bull; C&amp;R Tree basics for continuous targets&bull; Treatment of missing valuesEvaluation measures for supervised models&bull; Evaluation measures for categorical targets&bull; Evaluation measures for continuous targetsSupervised models: Statistical models for continuous targets - Linear regression&bull; Linear regression basics&bull; Include categorical predictors&bull; Treatment of missing valuesSupervised models: Statistical models for categorical targets - Logistic regression&bull; Logistic regression basics&bull; Include categorical predictors&bull; Treatment of missing valuesSupervised models: Black box models - Neural networks&bull; Neural network basics&bull; Include categorical and continuous predictors&bull; Treatment of missing valuesSupervised models: Black box models - Ensemble models&bull; Ensemble models basics&bull; Improve accuracy and generalizability by boosting and bagging&bull; Ensemble the best modelsUnsupervised models: K-Means and Kohonen&bull; K-Means basics&bull; Include categorical inputs in K-Means&bull; Treatment of missing values in K-Means&bull; Kohonen networks basics&bull; Treatment of missing values in KohonenUnsupervised models: TwoStep and Anomaly detection&bull; TwoStep basics&bull; TwoStep assumptions&bull; Find the best segmentation model automatically&bull; Anomaly detection basics&bull; Treatment of missing valuesAssociation models: Apriori&bull; Apriori basics&bull; Evaluation measures&bull; Treatment of missing valuesAssociation models: Sequence detection&bull; Sequence detection basics&bull; Treatment of missing valuesPreparing data for modeling&bull; Examine the quality of the data&bull; Select important predictors&bull; Balance the data<\/p>","essentials_plain":"- Knowledge of your business requirements","audience_plain":"- Data scientists\n- Business analysts\n- Clients who want to learn about machine learning models","contents_plain":"This 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.","outline_plain":"Introduction to machine learning models\u2022 Taxonomy of machine learning models\u2022 Identify measurement levels\u2022 Taxonomy of supervised models\u2022 Build and apply models in IBM SPSS ModelerSupervised models: Decision trees - CHAID\u2022 CHAID basics for categorical targets\u2022 Include categorical and continuous predictors\u2022 CHAID basics for continuous targets\u2022 Treatment of missing valuesSupervised models: Decision trees - C&R Tree\u2022 C&R Tree basics for categorical targets\u2022 Include categorical and continuous predictors\u2022 C&R Tree basics for continuous targets\u2022 Treatment of missing valuesEvaluation measures for supervised models\u2022 Evaluation measures for categorical targets\u2022 Evaluation measures for continuous targetsSupervised models: Statistical models for continuous targets - Linear regression\u2022 Linear regression basics\u2022 Include categorical predictors\u2022 Treatment of missing valuesSupervised models: Statistical models for categorical targets - Logistic regression\u2022 Logistic regression basics\u2022 Include categorical predictors\u2022 Treatment of missing valuesSupervised models: Black box models - Neural networks\u2022 Neural network basics\u2022 Include categorical and continuous predictors\u2022 Treatment of missing valuesSupervised models: Black box models - Ensemble models\u2022 Ensemble models basics\u2022 Improve accuracy and generalizability by boosting and bagging\u2022 Ensemble the best modelsUnsupervised models: K-Means and Kohonen\u2022 K-Means basics\u2022 Include categorical inputs in K-Means\u2022 Treatment of missing values in K-Means\u2022 Kohonen networks basics\u2022 Treatment of missing values in KohonenUnsupervised models: TwoStep and Anomaly detection\u2022 TwoStep basics\u2022 TwoStep assumptions\u2022 Find the best segmentation model automatically\u2022 Anomaly detection basics\u2022 Treatment of missing valuesAssociation models: Apriori\u2022 Apriori basics\u2022 Evaluation measures\u2022 Treatment of missing valuesAssociation models: Sequence detection\u2022 Sequence detection basics\u2022 Treatment of missing valuesPreparing data for modeling\u2022 Examine the quality of the data\u2022 Select important predictors\u2022 Balance the data","skill_level":"Beginner","version":"1","duration":{"unit":"d","value":2,"formatted":"2 days"},"pricelist":{"List Price":{"FR":{"country":"FR","currency":"EUR","taxrate":19.6,"price":1500},"CH":{"country":"CH","currency":"CHF","taxrate":8.1,"price":1890}}},"lastchanged":"2025-07-29T12:17:56+02:00","parenturl":"https:\/\/portal.flane.ch\/swisscom\/en\/json-courses","nexturl_course_schedule":"https:\/\/portal.flane.ch\/swisscom\/en\/json-course-schedule\/20050","source_lang":"en","source":"https:\/\/portal.flane.ch\/swisscom\/en\/json-course\/ibm-0a079g"}}