{"course":{"productid":20050,"modality":6,"active":true,"language":"fr","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>Introduction to machine learning models\n<\/p>\n<ul>\n<li>&bull; Taxonomy of machine learning models<\/li><li>&bull; Identify measurement levels<\/li><li>&bull; Taxonomy of supervised models<\/li><li>&bull; Build and apply models in IBM SPSS Modeler<\/li><\/ul><p>\nSupervised models: Decision trees - CHAID\n<\/p>\n<ul>\n<li>&bull; CHAID basics for categorical targets<\/li><li>&bull; Include categorical and continuous predictors<\/li><li>&bull; CHAID basics for continuous targets<\/li><li>&bull; Treatment of missing values<\/li><\/ul><p>\nSupervised models: Decision trees - C&amp;R Tree\n<\/p>\n<ul>\n<li>&bull; C&amp;R Tree basics for categorical targets<\/li><li>&bull; Include categorical and continuous predictors<\/li><li>&bull; C&amp;R Tree basics for continuous targets<\/li><li>&bull; Treatment of missing values<\/li><\/ul><p>\nEvaluation measures for supervised models\n<\/p>\n<ul>\n<li>&bull; Evaluation measures for categorical targets<\/li><li>&bull; Evaluation measures for continuous targets<\/li><\/ul><p>Supervised models: Statistical models for continuous targets - Linear regression\n<\/p>\n<ul>\n<li>&bull; Linear regression basics<\/li><li>&bull; Include categorical predictors<\/li><li>&bull; Treatment of missing values<\/li><\/ul><p>\nSupervised models: Statistical models for categorical targets - Logistic regression\n<\/p>\n<ul>\n<li>&bull; Logistic regression basics<\/li><li>&bull; Include categorical predictors<\/li><li>&bull; Treatment of missing values<\/li><\/ul><p>\nSupervised models: Black box models - Neural networks\n<\/p>\n<ul>\n<li>&bull; Neural network basics<\/li><li>&bull; Include categorical and continuous predictors<\/li><li>&bull; Treatment of missing values<\/li><\/ul><p>\nSupervised models: Black box models - Ensemble models\n<\/p>\n<ul>\n<li>&bull; Ensemble models basics<\/li><li>&bull; Improve accuracy and generalizability by boosting and bagging<\/li><li>&bull; Ensemble the best models<\/li><\/ul><p>\nUnsupervised models: K-Means and Kohonen\n<\/p>\n<ul>\n<li>&bull; K-Means basics<\/li><li>&bull; Include categorical inputs in K-Means<\/li><li>&bull; Treatment of missing values in K-Means<\/li><li>&bull; Kohonen networks basics<\/li><li>&bull; Treatment of missing values in Kohonen<\/li><\/ul><p>\nUnsupervised models: TwoStep and Anomaly detection\n<\/p>\n<ul>\n<li>&bull; TwoStep basics<\/li><li>&bull; TwoStep assumptions<\/li><li>&bull; Find the best segmentation model automatically<\/li><li>&bull; Anomaly detection basics<\/li><li>&bull; Treatment of missing values<\/li><\/ul><p>\nAssociation models: Apriori\n<\/p>\n<ul>\n<li>&bull; Apriori basics<\/li><li>&bull; Evaluation measures<\/li><li>&bull; Treatment of missing values<\/li><\/ul><p>\nAssociation models: Sequence detection\n<\/p>\n<ul>\n<li>&bull; Sequence detection basics<\/li><li>&bull; Treatment of missing values<\/li><\/ul><p>\nPreparing data for modeling\n<\/p>\n<ul>\n<li>&bull; Examine the quality of the data<\/li><li>&bull; Select important predictors<\/li><li>&bull; Balance the data<\/li><\/ul>","outline":"<p>Introduction to machine learning models\n<\/p>\n<ul>\n<li>&bull; Taxonomy of machine learning models<\/li><li>&bull; Identify measurement levels<\/li><li>&bull; Taxonomy of supervised models<\/li><li>&bull; Build and apply models in IBM SPSS Modeler<\/li><\/ul><p>\nSupervised models: Decision trees - CHAID\n<\/p>\n<ul>\n<li>&bull; CHAID basics for categorical targets<\/li><li>&bull; Include categorical and continuous predictors<\/li><li>&bull; CHAID basics for continuous targets<\/li><li>&bull; Treatment of missing values<\/li><\/ul><p>\nSupervised models: Decision trees - C&amp;R Tree\n<\/p>\n<ul>\n<li>&bull; C&amp;R Tree basics for categorical targets<\/li><li>&bull; Include categorical and continuous predictors<\/li><li>&bull; C&amp;R Tree basics for continuous targets<\/li><li>&bull; Treatment of missing values<\/li><\/ul><p>\nEvaluation measures for supervised models\n<\/p>\n<ul>\n<li>&bull; Evaluation measures for categorical targets<\/li><li>&bull; Evaluation measures for continuous targets<\/li><\/ul><p>Supervised models: Statistical models for continuous targets - Linear regression\n<\/p>\n<ul>\n<li>&bull; Linear regression basics<\/li><li>&bull; Include categorical predictors<\/li><li>&bull; Treatment of missing values<\/li><\/ul><p>\nSupervised models: Statistical models for categorical targets - Logistic regression\n<\/p>\n<ul>\n<li>&bull; Logistic regression basics<\/li><li>&bull; Include categorical predictors<\/li><li>&bull; Treatment of missing values<\/li><\/ul><p>\nSupervised models: Black box models - Neural networks\n<\/p>\n<ul>\n<li>&bull; Neural network basics<\/li><li>&bull; Include categorical and continuous predictors<\/li><li>&bull; Treatment of missing values<\/li><\/ul><p>\nSupervised models: Black box models - Ensemble models\n<\/p>\n<ul>\n<li>&bull; Ensemble models basics<\/li><li>&bull; Improve accuracy and generalizability by boosting and bagging<\/li><li>&bull; Ensemble the best models<\/li><\/ul><p>\nUnsupervised models: K-Means and Kohonen\n<\/p>\n<ul>\n<li>&bull; K-Means basics<\/li><li>&bull; Include categorical inputs in K-Means<\/li><li>&bull; Treatment of missing values in K-Means<\/li><li>&bull; Kohonen networks basics<\/li><li>&bull; Treatment of missing values in Kohonen<\/li><\/ul><p>\nUnsupervised models: TwoStep and Anomaly detection\n<\/p>\n<ul>\n<li>&bull; TwoStep basics<\/li><li>&bull; TwoStep assumptions<\/li><li>&bull; Find the best segmentation model automatically<\/li><li>&bull; Anomaly detection basics<\/li><li>&bull; Treatment of missing values<\/li><\/ul><p>\nAssociation models: Apriori\n<\/p>\n<ul>\n<li>&bull; Apriori basics<\/li><li>&bull; Evaluation measures<\/li><li>&bull; Treatment of missing values<\/li><\/ul><p>\nAssociation models: Sequence detection\n<\/p>\n<ul>\n<li>&bull; Sequence detection basics<\/li><li>&bull; Treatment of missing values<\/li><\/ul><p>\nPreparing data for modeling\n<\/p>\n<ul>\n<li>&bull; Examine the quality of the data<\/li><li>&bull; Select important predictors<\/li><li>&bull; Balance the data<\/li><\/ul>","summary":"<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>","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":"Introduction to machine learning models\n\n\n\n- \u2022 Taxonomy of machine learning models\n- \u2022 Identify measurement levels\n- \u2022 Taxonomy of supervised models\n- \u2022 Build and apply models in IBM SPSS Modeler\n\nSupervised models: Decision trees - CHAID\n\n\n\n- \u2022 CHAID basics for categorical targets\n- \u2022 Include categorical and continuous predictors\n- \u2022 CHAID basics for continuous targets\n- \u2022 Treatment of missing values\n\nSupervised models: Decision trees - C&R Tree\n\n\n\n- \u2022 C&R Tree basics for categorical targets\n- \u2022 Include categorical and continuous predictors\n- \u2022 C&R Tree basics for continuous targets\n- \u2022 Treatment of missing values\n\nEvaluation measures for supervised models\n\n\n\n- \u2022 Evaluation measures for categorical targets\n- \u2022 Evaluation measures for continuous targets\nSupervised models: Statistical models for continuous targets - Linear regression\n\n\n\n- \u2022 Linear regression basics\n- \u2022 Include categorical predictors\n- \u2022 Treatment of missing values\n\nSupervised models: Statistical models for categorical targets - Logistic regression\n\n\n\n- \u2022 Logistic regression basics\n- \u2022 Include categorical predictors\n- \u2022 Treatment of missing values\n\nSupervised models: Black box models - Neural networks\n\n\n\n- \u2022 Neural network basics\n- \u2022 Include categorical and continuous predictors\n- \u2022 Treatment of missing values\n\nSupervised models: Black box models - Ensemble models\n\n\n\n- \u2022 Ensemble models basics\n- \u2022 Improve accuracy and generalizability by boosting and bagging\n- \u2022 Ensemble the best models\n\nUnsupervised models: K-Means and Kohonen\n\n\n\n- \u2022 K-Means basics\n- \u2022 Include categorical inputs in K-Means\n- \u2022 Treatment of missing values in K-Means\n- \u2022 Kohonen networks basics\n- \u2022 Treatment of missing values in Kohonen\n\nUnsupervised models: TwoStep and Anomaly detection\n\n\n\n- \u2022 TwoStep basics\n- \u2022 TwoStep assumptions\n- \u2022 Find the best segmentation model automatically\n- \u2022 Anomaly detection basics\n- \u2022 Treatment of missing values\n\nAssociation models: Apriori\n\n\n\n- \u2022 Apriori basics\n- \u2022 Evaluation measures\n- \u2022 Treatment of missing values\n\nAssociation models: Sequence detection\n\n\n\n- \u2022 Sequence detection basics\n- \u2022 Treatment of missing values\n\nPreparing data for modeling\n\n\n\n- \u2022 Examine the quality of the data\n- \u2022 Select important predictors\n- \u2022 Balance the data","outline_plain":"Introduction to machine learning models\n\n\n\n- \u2022 Taxonomy of machine learning models\n- \u2022 Identify measurement levels\n- \u2022 Taxonomy of supervised models\n- \u2022 Build and apply models in IBM SPSS Modeler\n\nSupervised models: Decision trees - CHAID\n\n\n\n- \u2022 CHAID basics for categorical targets\n- \u2022 Include categorical and continuous predictors\n- \u2022 CHAID basics for continuous targets\n- \u2022 Treatment of missing values\n\nSupervised models: Decision trees - C&R Tree\n\n\n\n- \u2022 C&R Tree basics for categorical targets\n- \u2022 Include categorical and continuous predictors\n- \u2022 C&R Tree basics for continuous targets\n- \u2022 Treatment of missing values\n\nEvaluation measures for supervised models\n\n\n\n- \u2022 Evaluation measures for categorical targets\n- \u2022 Evaluation measures for continuous targets\nSupervised models: Statistical models for continuous targets - Linear regression\n\n\n\n- \u2022 Linear regression basics\n- \u2022 Include categorical predictors\n- \u2022 Treatment of missing values\n\nSupervised models: Statistical models for categorical targets - Logistic regression\n\n\n\n- \u2022 Logistic regression basics\n- \u2022 Include categorical predictors\n- \u2022 Treatment of missing values\n\nSupervised models: Black box models - Neural networks\n\n\n\n- \u2022 Neural network basics\n- \u2022 Include categorical and continuous predictors\n- \u2022 Treatment of missing values\n\nSupervised models: Black box models - Ensemble models\n\n\n\n- \u2022 Ensemble models basics\n- \u2022 Improve accuracy and generalizability by boosting and bagging\n- \u2022 Ensemble the best models\n\nUnsupervised models: K-Means and Kohonen\n\n\n\n- \u2022 K-Means basics\n- \u2022 Include categorical inputs in K-Means\n- \u2022 Treatment of missing values in K-Means\n- \u2022 Kohonen networks basics\n- \u2022 Treatment of missing values in Kohonen\n\nUnsupervised models: TwoStep and Anomaly detection\n\n\n\n- \u2022 TwoStep basics\n- \u2022 TwoStep assumptions\n- \u2022 Find the best segmentation model automatically\n- \u2022 Anomaly detection basics\n- \u2022 Treatment of missing values\n\nAssociation models: Apriori\n\n\n\n- \u2022 Apriori basics\n- \u2022 Evaluation measures\n- \u2022 Treatment of missing values\n\nAssociation models: Sequence detection\n\n\n\n- \u2022 Sequence detection basics\n- \u2022 Treatment of missing values\n\nPreparing data for modeling\n\n\n\n- \u2022 Examine the quality of the data\n- \u2022 Select important predictors\n- \u2022 Balance the data","summary_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.","skill_level":"Beginner","version":"1","duration":{"unit":"d","value":2,"formatted":"2 jours"},"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\/fr\/json-courses","nexturl_course_schedule":"https:\/\/portal.flane.ch\/swisscom\/fr\/json-course-schedule\/20050","source_lang":"fr","source":"https:\/\/portal.flane.ch\/swisscom\/fr\/json-course\/ibm-0a079g"}}