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

 

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

Skill Level: Basic

Modality:  CR - Classroom based Trainng or ILO - Instructor Led Online Class

Duration: 2 Day/s

Starting Price:  $ - 1,306

Overview:

This course teaches how to use machine learning models to predict categorical and continuous targets, to create natural groupings, and to find associations.

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.

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Target Audience:

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Data scientists

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Business analysts

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Clients who want to learn about machine learning models

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Prerequisites: _x000D_

Knowledge of your business requirements

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Topic: 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 - CHAIDCHAID 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 regressionLinear 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

 Association models: Sequence detectionSequence detection basics

Treatment of missing values

 Supervised models: Black box models - Neural networksNeural 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 KohonenK-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 detectionTwoStep basics

TwoStep assumptions

Find the best segmentation model automatically

Anomaly detection basics

Treatment of missing values

 Association models: AprioriApriori basics

Evaluation measures

Treatment of missing values

 Preparing data for modeling

Examine the quality of the data

Select important predictors

Balance the data

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IBM Training

Objective: 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 - CHAIDCHAID 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 regressionLinear 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

 Association models: Sequence detectionSequence detection basics

Treatment of missing values

 Supervised models: Black box models - Neural networksNeural 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 KohonenK-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 detectionTwoStep basics

TwoStep assumptions

Find the best segmentation model automatically

Anomaly detection basics

Treatment of missing values  

 Association models: AprioriApriori basics

Evaluation measures

Treatment of missing values

 Preparing data for modeling

Examine the quality of the data 

Select important predictors 

Balance the data

Category: Data, Analytics, and AI

 

Product Name:

IBM SPSS Modeler

 

Badge and Certification Info:

Badge Title: Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2) - Code: 0A079G

Badge ID: 76dab783-df59-41d7-b2f5-241aa1f0e012

 

Brand: Analytics

 

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