EncartaLabs

IBM SPSS Modeler - Machine Learning Models

IBM SPSS Modeler Machine Learning Models - Essentials training 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.

IBM SPSS Modeler Machine Learning Models - Advanced training course presents advanced models available in IBM SPSS Modeler. The attendees are 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.

For IBM SPSS Modeler Machine Learning Models - Essentials workshop
  • Knowledge of business requirements
For IBM SPSS Modeler Machine Learning Models - Advanced workshop
  • Knowledge of business requirements
  • Attend IBM SPSS Modeler Machine Learning Models - Essentials course or equivalent knowledge of how to import, explore, and prepare data with IBM SPSS Modeler, and know the basics of modeling.
For IBM SPSS Modeler Machine Learning Models - Essentials class
  • Data scientists
  • Business analysts
  • Clients who want to learn about machine learning models
For IBM SPSS Modeler Machine Learning Models - Advanced class
  • Data scientists
  • Business analysts
  • Experienced users of IBM SPSS Modeler who want to learn about advanced techniques in the software

COURSE AGENDA

IBM SPSS Modeler Machine Learning Models - Essentials
(Duration : 2 Days)

1

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
2

Supervised models: Decision trees - CHAID

  • CHAID basics for categorical targets
  • Include categorical and continuous predictors
  • CHAID basics for continuous targets
  • Treatment of missing values
3

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
4

Evaluation measures for supervised models

  • Evaluation measures for categorical targets
  • Evaluation measures for continuous targets
5

Supervised models: Statistical models for continuous targets - Linear regression

  • Linear regression basics
  • Include categorical predictors
  • Treatment of missing values
6

Supervised models: Statistical models for categorical targets - Logistic regression

  • Logistic regression basics
  • Include categorical predictors
  • Treatment of missing values
7

Supervised models: Black box models - Neural networks

  • Neural network basics
  • Include categorical and continuous predictors
  • Treatment of missing values
8

Supervised models: Black box models - Ensemble models

  • Ensemble models basics
  • Improve accuracy and generalizability by boosting and bagging
  • Ensemble the best models
9

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
10

Unsupervised models: TwoStep and Anomaly detection

  • TwoStep basics
  • TwoStep assumptions
  • Find the best segmentation model automatically
  • Anomaly detection basics
  • Treatment of missing values
11

Association models: Apriori

  • Apriori basics
  • Evaluation measures
  • Treatment of missing values
12

Association models: Sequence detection

  • Sequence detection basics
  • Treatment of missing values
13

Preparing data for modeling

  • Examine the quality of the data
  • Select important predictors
  • Balance the data
IBM SPSS Modeler Machine Learning Models - Advanced
(Duration : 1 Day)

1

Introduction to advanced machine learning models

  • Taxonomy of models
  • Overview of supervised models
  • Overview of models to create natural groupings
2

Group fields: Factor Analysis and Principal Component Analysis

  • Factor Analysis basics
  • Principal Components basics
  • Assumptions of Factor Analysis
  • Key issues in Factor Analysis
  • Improve the interpretability
  • Factor and component scores
3

Predict targets with Nearest Neighbor Analysis

  • Nearest Neighbor Analysis basics
  • Key issues in Nearest Neighbor Analysis
  • Assess model fit
4

Explore advanced supervised models

  • Support Vector Machines basics
  • Random Trees basics
  • XGBoost basics
5

Introduction to Generalized Linear Models

  • Generalized Linear Models
  • Available distributions
  • Available link functions
6

Combine supervised models

  • Combine models with the Ensemble node
  • Identify ensemble methods for categorical targets
  • Identify ensemble methods for flag targets
  • Identify ensemble methods for continuous targets
  • Meta-level modeling
7

Use external machine learning models

  • IBM SPSS Modeler Extension nodes
  • Use external machine learning programs in IBM SPSS Modeler
8

Analyze text data

  • Text Mining and Data Science
  • Text Mining applications
  • Modeling with text data

Encarta Labs Advantage

  • One Stop Corporate Training Solution Providers for over 4,000 Modules on a variety of subjects
  • All courses are delivered by Industry Veterans
  • Get jumpstarted from newbie to production ready in a matter of few days
  • Trained more than 50,000 Corporate executives across the Globe
  • All our trainings are conducted in workshop mode with more focus on hands-on sessions

View our other course offerings by visiting http://encartalabs.com/course-catalogue-all.php

Contact us for delivering this course as a public/open-house workshop/online training for a group of 10+ candidates.

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