IBM Training for Advanced Machine Learning Models Using IBM

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

IBM Training for Advanced Machine Learning Models Using IBM SPSS Modeler (V18.2)

Skill Level: Advanced

Modality: SPVC – Self Paced Virtual Class

Duration: 1 Day/s

Starting Price: $ – 517

Overview:

This course teaches how to use advanced techniques in IBM SPSS Modeler 18.2

Contains PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.This course presents advanced models available in IBM SPSS Modeler. The participant 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.If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course. http://www.ibm.com/training/terms

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Target Audience:

Data scientists

Business analysts

Experienced users of IBM SPSS Modeler who want to learn about advanced techniques in the software

Prerequisites:

Knowledge of your business requirements
Required: IBM SPSS Modeler Foundations (V18.2) course (0A069G/0E069G) or equivalent knowledge of how to import, explore, and prepare data with IBM SPSS Modeler v18.2, and know the basics of modeling.
Recommended: Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2) course (0A079G/0E079G), or equivalent knowledge or experience with the product about supervised machine learning models (CHAID, C&R Tree, Regression, Random Trees, Neural Net, XGBoost), unsupervised machine learning models (TwoStep Cluster), and association machine learning models such as APriori.

Topic: Introduction to advanced machine learning models

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

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
Predict targets with Nearest Neighbor Analysis
Nearest Neighbor Analysis basics
Key issues in Nearest Neighbor Analysis
Assess model fit
Explore advanced supervised models
Support Vector Machines basics
Random Trees basics
XGBoost basics

Introduction to Generalized Linear Models

Generalized Linear Models
Available distributions
Available link functions
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
Use external machine learning models
IBM SPSS Modeler Extension nodes
Use external machine learning programs in IBM SPSS Modeler
Analyze text data
Text Mining and Data Science
Text Mining applications
Modeling with text data

IBM Training

Objective: Introduction to advanced machine learning models

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

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
Predict targets with Nearest Neighbor Analysis
Nearest Neighbor Analysis basics
Key issues in Nearest Neighbor Analysis
Assess model fit
Explore advanced supervised models
Support Vector Machines basics
Random Trees basics
XGBoost basics

Introduction to Generalized Linear Models

Generalized Linear Models
Available distributions
Available link functions
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
Use external machine learning models
IBM SPSS Modeler Extension nodes
Use external machine learning programs in IBM SPSS Modeler
Analyze text data
Text Mining and Data Science
Text Mining applications
Modeling with text data

Category: Data, Analytics, and AI

Product Name:

IBM SPSS Modeler

Badge and Certification Info:

Badge Title:

Badge ID:

Brand: Analytics

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