0A079G Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2)
Duration: 2 Days
Level: Basic
Audience: Business Analyst, Data Scientist, Research
Next Sessions
| Start (YYYY-MM-DD) | End (YYYY-MM-DD) | Language | Amount | |
|---|---|---|---|---|
| 2024-05-09 | 2024-05-10 | English | 1450 EUR | |
| 2024-05-23 | 2024-05-24 | English | 1450 EUR | |
| 2024-06-06 | 2024-06-07 | English | 1450 EUR | |
| 2024-06-20 | 2024-06-21 | English | 1450 EUR | |
| 2024-07-04 | 2024-07-05 | English | 1450 EUR | |
| 2024-07-18 | 2024-07-19 | English | 1450 EUR | |
| 2024-08-01 | 2024-08-02 | English | 1450 EUR | |
| 2024-08-15 | 2024-08-16 | English | 1450 EUR | |
| 2024-08-29 | 2024-08-30 | English | 1450 EUR | |
| 2024-09-12 | 2024-09-13 | English | 1450 EUR | |
| 2024-09-26 | 2024-09-27 | English | 1450 EUR | |
| 2024-10-10 | 2024-10-11 | English | 1450 EUR | |
| 2024-10-24 | 2024-10-25 | English | 1450 EUR | |
| 2024-11-07 | 2024-11-08 | English | 1450 EUR | |
| 2024-11-21 | 2024-11-22 | English | 1450 EUR | |
| 2024-12-05 | 2024-12-06 | English | 1450 EUR | |
| 2024-12-19 | 2024-12-20 | English | 1450 EUR |
Overview
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.
Prerequisites
- Knowledge of your business requirements
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
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