Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2)

Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2)0A079GIBIBMIB-0A079G1<ul> <li>Knowledge of your business requirements</li></ul><ul> <li>Data scientists</li><li>Business analysts</li><li>Clients who want to learn about machine learning models</li></ul>Introduction to machine learning models <ul> <li>• Taxonomy of machine learning models</li><li>• Identify measurement levels</li><li>• Taxonomy of supervised models</li><li>• Build and apply models in IBM SPSS Modeler</li></ul> Supervised models: Decision trees - CHAID <ul> <li>• CHAID basics for categorical targets</li><li>• Include categorical and continuous predictors</li><li>• CHAID basics for continuous targets</li><li>• Treatment of missing values</li></ul> Supervised models: Decision trees - C&R Tree <ul> <li>• C&R Tree basics for categorical targets</li><li>• Include categorical and continuous predictors</li><li>• C&R Tree basics for continuous targets</li><li>• Treatment of missing values</li></ul> Evaluation measures for supervised models <ul> <li>• Evaluation measures for categorical targets</li><li>• Evaluation measures for continuous targets</li></ul>Supervised models: Statistical models for continuous targets - Linear regression <ul> <li>• Linear regression basics</li><li>• Include categorical predictors</li><li>• Treatment of missing values</li></ul> Supervised models: Statistical models for categorical targets - Logistic regression <ul> <li>• Logistic regression basics</li><li>• Include categorical predictors</li><li>• Treatment of missing values</li></ul> Supervised models: Black box models - Neural networks <ul> <li>• Neural network basics</li><li>• Include categorical and continuous predictors</li><li>• Treatment of missing values</li></ul> Supervised models: Black box models - Ensemble models <ul> <li>• Ensemble models basics</li><li>• Improve accuracy and generalizability by boosting and bagging</li><li>• Ensemble the best models</li></ul> Unsupervised models: K-Means and Kohonen <ul> <li>• K-Means basics</li><li>• Include categorical inputs in K-Means</li><li>• Treatment of missing values in K-Means</li><li>• Kohonen networks basics</li><li>• Treatment of missing values in Kohonen</li></ul> Unsupervised models: TwoStep and Anomaly detection <ul> <li>• TwoStep basics</li><li>• TwoStep assumptions</li><li>• Find the best segmentation model automatically</li><li>• Anomaly detection basics</li><li>• Treatment of missing values</li></ul> Association models: Apriori <ul> <li>• Apriori basics</li><li>• Evaluation measures</li><li>• Treatment of missing values</li></ul> Association models: Sequence detection <ul> <li>• Sequence detection basics</li><li>• Treatment of missing values</li></ul> Preparing data for modeling <ul> <li>• Examine the quality of the data</li><li>• Select important predictors</li><li>• Balance the data</li></ul>Introduction to machine learning models <ul> <li>• Taxonomy of machine learning models</li><li>• Identify measurement levels</li><li>• Taxonomy of supervised models</li><li>• Build and apply models in IBM SPSS Modeler</li></ul> Supervised models: Decision trees - CHAID <ul> <li>• CHAID basics for categorical targets</li><li>• Include categorical and continuous predictors</li><li>• CHAID basics for continuous targets</li><li>• Treatment of missing values</li></ul> Supervised models: Decision trees - C&R Tree <ul> <li>• C&R Tree basics for categorical targets</li><li>• Include categorical and continuous predictors</li><li>• C&R Tree basics for continuous targets</li><li>• Treatment of missing values</li></ul> Evaluation measures for supervised models <ul> <li>• Evaluation measures for categorical targets</li><li>• Evaluation measures for continuous targets</li></ul>Supervised models: Statistical models for continuous targets - Linear regression <ul> <li>• Linear regression basics</li><li>• Include categorical predictors</li><li>• Treatment of missing values</li></ul> Supervised models: Statistical models for categorical targets - Logistic regression <ul> <li>• Logistic regression basics</li><li>• Include categorical predictors</li><li>• Treatment of missing values</li></ul> Supervised models: Black box models - Neural networks <ul> <li>• Neural network basics</li><li>• Include categorical and continuous predictors</li><li>• Treatment of missing values</li></ul> Supervised models: Black box models - Ensemble models <ul> <li>• Ensemble models basics</li><li>• Improve accuracy and generalizability by boosting and bagging</li><li>• Ensemble the best models</li></ul> Unsupervised models: K-Means and Kohonen <ul> <li>• K-Means basics</li><li>• Include categorical inputs in K-Means</li><li>• Treatment of missing values in K-Means</li><li>• Kohonen networks basics</li><li>• Treatment of missing values in Kohonen</li></ul> Unsupervised models: TwoStep and Anomaly detection <ul> <li>• TwoStep basics</li><li>• TwoStep assumptions</li><li>• Find the best segmentation model automatically</li><li>• Anomaly detection basics</li><li>• Treatment of missing values</li></ul> Association models: Apriori <ul> <li>• Apriori basics</li><li>• Evaluation measures</li><li>• Treatment of missing values</li></ul> Association models: Sequence detection <ul> <li>• Sequence detection basics</li><li>• Treatment of missing values</li></ul> Preparing data for modeling <ul> <li>• Examine the quality of the data</li><li>• Select important predictors</li><li>• Balance the data</li></ul>- Knowledge of your business requirements- Data scientists - Business analysts - Clients who want to learn about machine learning modelsIntroduction 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 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 Association models: Sequence detection - • Sequence detection basics - • Treatment of missing values Preparing data for modeling - • Examine the quality of the data - • Select important predictors - • Balance the dataIntroduction 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 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 Association models: Sequence detection - • Sequence detection basics - • Treatment of missing values Preparing data for modeling - • Examine the quality of the data - • Select important predictors - • Balance the data2 jours1500.001890.00