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

Advanced Machine Learning Models Using IBM SPSS Modeler (V18.2)0A039GIBIBMIB-0A039G1<ul> <li>Knowledge of your business requirements</li><li>Required: IBM SPSS Modeler Foundations (V18.2) course (0A069G/0E069G) or equivalent knowledge of how to import, explore, and prepare data with IBM SPSS Modeler v18.2, and know the basics of modeling.</li><li>Recommended: Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2) course (0A079G/0E079G), or equivalent knowledge or experience with the product about supervised machine learning models (CHAID, C&R Tree, Regression, Random Trees, Neural Net, XGBoost), unsupervised machine learning models (TwoStep Cluster), and association machine learning models such as APriori.</li></ul><ul> <li>Data scientists</li><li>Business analysts</li><li>Experienced users of IBM SPSS Modeler who want to learn about advanced techniques in the software</li></ul>Introduction to advanced machine learning models <ul> <li>Taxonomy of models</li><li>Overview of supervised models</li><li>Overview of models to create natural groupings</li></ul> Group fields:  Factor Analysis and Principal Component Analysis <ul> <li>Factor Analysis basics</li><li>Principal Components basics</li><li>Assumptions of Factor Analysis</li><li>Key issues in Factor Analysis</li><li>Improve the interpretability</li><li>Factor and component scores</li></ul> Predict targets with Nearest Neighbor Analysis <ul> <li>Nearest Neighbor Analysis basics</li><li>Key issues in Nearest Neighbor Analysis</li><li>Assess model fit</li></ul> Explore advanced supervised models <ul> <li>Support Vector Machines basics</li><li>Random Trees basics</li><li>XGBoost basics</li></ul> Introduction to Generalized Linear Models <ul> <li>Generalized Linear Models</li><li>Available distributions</li><li>Available link functions</li></ul> Combine supervised models <ul> <li>Combine models with the Ensemble node</li><li>Identify ensemble methods for categorical targets</li><li>Identify ensemble methods for flag targets</li><li>Identify ensemble methods for continuous targets</li><li>Meta-level modeling</li></ul> Use external machine learning models <ul> <li>IBM SPSS Modeler Extension nodes</li><li>Use external machine learning programs in IBM SPSS Modeler</li></ul> Analyze text data <ul> <li>Text Mining and Data Science</li><li>Text Mining applications</li><li>Modeling with text data</li></ul>Introduction to advanced machine learning models <ul> <li>Taxonomy of models</li><li>Overview of supervised models</li><li>Overview of models to create natural groupings</li></ul> Group fields:  Factor Analysis and Principal Component Analysis <ul> <li>Factor Analysis basics</li><li>Principal Components basics</li><li>Assumptions of Factor Analysis</li><li>Key issues in Factor Analysis</li><li>Improve the interpretability</li><li>Factor and component scores</li></ul> Predict targets with Nearest Neighbor Analysis <ul> <li>Nearest Neighbor Analysis basics</li><li>Key issues in Nearest Neighbor Analysis</li><li>Assess model fit</li></ul> Explore advanced supervised models <ul> <li>Support Vector Machines basics</li><li>Random Trees basics</li><li>XGBoost basics</li></ul> Introduction to Generalized Linear Models <ul> <li>Generalized Linear Models</li><li>Available distributions</li><li>Available link functions</li></ul> Combine supervised models <ul> <li>Combine models with the Ensemble node</li><li>Identify ensemble methods for categorical targets</li><li>Identify ensemble methods for flag targets</li><li>Identify ensemble methods for continuous targets</li><li>Meta-level modeling</li></ul> Use external machine learning models <ul> <li>IBM SPSS Modeler Extension nodes</li><li>Use external machine learning programs in IBM SPSS Modeler</li></ul> Analyze text data <ul> <li>Text Mining and Data Science</li><li>Text Mining applications</li><li>Modeling with text data</li></ul>- Knowledge of your business requirements - Required: IBM SPSS Modeler Foundations (V18.2) course (0A069G/0E069G) or equivalent knowledge of how to import, explore, and prepare data with IBM SPSS Modeler v18.2, and know the basics of modeling. - Recommended: Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2) course (0A079G/0E079G), or equivalent knowledge or experience with the product about supervised machine learning models (CHAID, C&R Tree, Regression, Random Trees, Neural Net, XGBoost), unsupervised machine learning models (TwoStep Cluster), and association machine learning models such as APriori.- Data scientists - Business analysts - Experienced users of IBM SPSS Modeler who want to learn about advanced techniques in the softwareIntroduction to advanced machine learning models - Taxonomy of models - Overview of supervised models - Overview of models to create natural groupings Group fields: Factor Analysis and Principal Component Analysis - Factor Analysis basics - Principal Components basics - Assumptions of Factor Analysis - Key issues in Factor Analysis - Improve the interpretability - Factor and component scores Predict targets with Nearest Neighbor Analysis - Nearest Neighbor Analysis basics - Key issues in Nearest Neighbor Analysis - Assess model fit Explore advanced supervised models - Support Vector Machines basics - Random Trees basics - XGBoost basics Introduction to Generalized Linear Models - Generalized Linear Models - Available distributions - Available link functions Combine supervised models - Combine models with the Ensemble node - Identify ensemble methods for categorical targets - Identify ensemble methods for flag targets - Identify ensemble methods for continuous targets - Meta-level modeling Use external machine learning models - IBM SPSS Modeler Extension nodes - Use external machine learning programs in IBM SPSS Modeler Analyze text data - Text Mining and Data Science - Text Mining applications - Modeling with text dataIntroduction to advanced machine learning models - Taxonomy of models - Overview of supervised models - Overview of models to create natural groupings Group fields: Factor Analysis and Principal Component Analysis - Factor Analysis basics - Principal Components basics - Assumptions of Factor Analysis - Key issues in Factor Analysis - Improve the interpretability - Factor and component scores Predict targets with Nearest Neighbor Analysis - Nearest Neighbor Analysis basics - Key issues in Nearest Neighbor Analysis - Assess model fit Explore advanced supervised models - Support Vector Machines basics - Random Trees basics - XGBoost basics Introduction to Generalized Linear Models - Generalized Linear Models - Available distributions - Available link functions Combine supervised models - Combine models with the Ensemble node - Identify ensemble methods for categorical targets - Identify ensemble methods for flag targets - Identify ensemble methods for continuous targets - Meta-level modeling Use external machine learning models - IBM SPSS Modeler Extension nodes - Use external machine learning programs in IBM SPSS Modeler Analyze text data - Text Mining and Data Science - Text Mining applications - Modeling with text data1 jour750.00990.00