0A079G Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2)
Duração: 2 Dias
Nível: Básico
Audiência: Business Analyst, Data Scientist, Research
PRÓXIMAS SESSÕES
| Início (AAAA-MM-DD) | Fim (AAAA-MM-DD) | Língua | Preço | |
|---|---|---|---|---|
| 2023-11-09 | 2023-11-10 | Português | 1450 EUR | |
| 2023-11-23 | 2023-11-24 | Português | 1450 EUR | |
| 2023-12-07 | 2023-12-08 | Português | 1450 EUR | |
| 2023-12-21 | 2023-12-22 | Português | 1450 EUR | |
| 2024-01-18 | 2024-01-19 | Português | 1450 EUR | |
| 2024-02-01 | 2024-02-02 | Português | 1450 EUR | |
| 2024-02-15 | 2024-02-16 | Português | 1450 EUR | |
| 2024-02-29 | 2024-03-01 | Português | 1450 EUR | |
| 2024-03-14 | 2024-03-15 | Português | 1450 EUR | |
| 2024-03-28 | 2024-03-29 | Português | 1450 EUR | |
| 2024-04-11 | 2024-04-12 | Português | 1450 EUR | |
| 2024-04-25 | 2024-04-26 | Português | 1450 EUR | |
| 2024-05-09 | 2024-05-10 | Português | 1450 EUR | |
| 2024-05-23 | 2024-05-24 | Português | 1450 EUR | |
| 2024-06-06 | 2024-06-07 | Português | 1450 EUR | |
| 2024-06-20 | 2024-06-21 | Português | 1450 EUR |
SÍNTESE
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.
PREREQUISITOS
- 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