IBM SPSS Modeler - Machine Learning Models Training Course and Workshop in Bangalore, Mysore, Chennai, Hyderabad, Pune, Mumbai, Delhi, Noida, Gurgaon, Kolkata

IBM SPSS Modeler Machine Learning Models - Essentials training 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.

The IBM SPSS Modeler Machine Learning Models - Advanced training course presents advanced models available in IBM SPSS Modeler. Then you are first introduced to a technique named PCA/Factor, to reduce the number of fields to a number of core factors, referred to as components or factors. The subsequent topics focus on supervised models, including Support Vector Machines, Random Trees, and XGBoost. Methods are reviewed on how to analyze text data, combine individual models into a single model, and how to enhance the power of IBM SPSS Modeler by adding external models, developed in Python or R, to the Modeling palette.

For IBM SPSS Modeler Machine Learning Models - Essentials workshop

Knowledge of business requirements

For IBM SPSS Modeler Machine Learning Models - Advanced workshop

Knowledge of business requirements
Attend IBM SPSS Modeler Machine Learning Models - Essentials course or equivalent knowledge of how to import, explore, and prepare data with IBM SPSS Modeler, and know the basics of modeling.

For IBM SPSS Modeler Machine Learning Models - Essentials class

Data scientists
Business analysts
Clients who want to learn about machine learning models

For IBM SPSS Modeler Machine Learning Models - Advanced class

Data scientists
Business analysts
Experienced users of IBM SPSS Modeler who want to learn about advanced techniques in the software

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

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 data

Introduction 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 data

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IBM SPSS Modeler - Machine Learning Models

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