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

Description

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.

Objectives

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

Association models: Sequence detection

Sequence detection basics
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

Preparing data for modeling
Examine the quality of the data
Select important predictors
Balance the data

Audience

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

Prerequisites

Knowledge of your business requirements

Programme

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