IBM Training for Introduction to Machine Learning Models

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 Training 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:

Data scientists

Business analysts

Clients who want to learn about machine learning models

Prerequisites:

Knowledge of your business requirements

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

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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