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

Duration: 
2 days
Codes: 
0A079G
Versions: 
V18.2

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.

Audience

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

Skills Gained

  • Introduction to machine learning models
  • Evaluation measures for supervised models
  • Supervised models: Statistical models for continuous targets - Linear regression
  • Supervised models: Statistical models for categorical targets - Logistic regression
  • Supervised models: Black box models - Neural networks
  • Supervised models: Black box models - Ensemble models
  • Unsupervised models: K-Means and Kohonen
  • Unsupervised models: TwoStep and Anomaly detection
  • Association models: Sequence detection
  • Preparing data for modeling

Prerequisites

  • Knowledge of your business requirements

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

  • CHAID basics for categorical targets
  • Include categorical and continuous predictors
  • CHAID basics for continuous targets

Treatment of missing values

  • C&R Tree basics for categorical targets
  • C&R Tree basics for continuous targets
  • 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
  • Supervised models: Statistical models for categorical targets - Logistic regression
  • Logistic regression basics

Association models: Sequence detection

  • Sequence detection basics

Supervised models: Black box models - Neural networks

  • Neural network basics

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
  • Evaluation measures

Preparing data for modeling

  • Examine the quality of the data
  • Select important predictors

Balance the data

Related Courses

 

Thinking about Onsite?

If you need training for 3 or more people, you should ask us about onsite training. Putting aside the obvious location benefit, content can be customised to better meet your business objectives and more can be covered than in a public classroom. It's a cost effective option.

Submit an enquiry from any page on this site, and let us know you are interested in the requirements box, or simply mention it when we contact you.

ITILv3, RESILIA, PRINCE2, PRINCE2 Agile, AgileSHIFT, MSP, M_o_R, P3M3, P3O, MoP, MoV courses on this page are offered by QA Affiliate of AXELOS Limited. ITIL, RESILIA, PRINCE2, PRINCE2 Agile, AgileSHIFT, MSP, M_o_R, P3M3, P3O,MoP, MoV are registered trademarks of AXELOS Limited. All rights reserved.