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0A079G – Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2)

Home / 0A079G – Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2)

Course Outline:

Course Outline

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

Course Audience & Prerequisites:

Audience: 

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

Prerequisites:

  • Knowledge of your business requirements

Course Offerings:

Instructor Led In Classroom

Newcomp can directly deliver  IBM Business Analytics courses for Business Intelligence, Performance Management, and IBM Advanced Analytics through the use of in-class training facilities.

Currently,  in-class courses are offered in Markham, Ottawa, Vancouver, Halifax, and Edmonton. Please note that classes can be added to new areas based on demand.

Instructor Led Online

Students receive the same quality as an in-class course, with a live instructor and the ability to participate in hands-on labs through real-life examples

ILOs help cut costs by reducing time and travel as they can be taken from home or the office and require only the use of a computer, high-speed wired internet and a headset.

Self Paced

Students can receive the same high-quality training, with the same courseware at their own speed and schedule with SPVC.  Individuals with busy schedules can complete a course over a 30-day timeframe at a lower price than in-class or ILO courses. Please note that there is no live interaction with an instructor in this format.

  • Course Outline
  • Course Audience & Prerequisites
  • Course Offerings
  • Related Courses

Course Outline

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

Audience: 

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

Prerequisites:

  • Knowledge of your business requirements

Instructor Led In Classroom

Newcomp can directly deliver  IBM Business Analytics courses for Business Intelligence, Performance Management, and IBM Advanced Analytics through the use of in-class training facilities.

Currently,  in-class courses are offered in Markham, Ottawa, Vancouver, Halifax, and Edmonton. Please note that classes can be added to new areas based on demand.

Instructor Led Online

Students receive the same quality as an in-class course, with a live instructor and the ability to participate in hands-on labs through real-life examples

ILOs help cut costs by reducing time and travel as they can be taken from home or the office and require only the use of a computer, high-speed wired internet and a headset.

Self Paced

Students can receive the same high-quality training, with the same courseware at their own speed and schedule with SPVC.  Individuals with busy schedules can complete a course over a 30-day timeframe at a lower price than in-class or ILO courses. Please note that there is no live interaction with an instructor in this format.

0A039G – Advanced Machine Learning Models Using IBM SPSS Modeler (V18.2)

This course presents advanced models available in IBM SPSS Modeler. The student is first introduced to a technique named PCA/Factor, to reduce the number of fields to a number of core factors, referred to as components or factors. The next topics focus on supervised models, including Support Vector Machines, Random Trees, and XGBoost. Methods are reviewed on how to analyze text data, combine individual models into a single model, and how to enhance the power of IBM SPSS Modeler by adding external models, developed in Python or R, to the Modeling palette.

0A069G – IBM SPSS Modeler Foundations (V18.2)

This course provides the foundations of using IBM SPSS Modeler and introduces the participant to data science. The principles and practice of data science are illustrated using the CRISP-DM methodology. The course provides training in the basics of how to import, explore, and prepare data with IBM SPSS Modeler v18.2, and introduces the student to modeling.