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

Course content updated by LearnQuest
Price
525 - 815 USD
8 Hours
0E039GS
Self-Paced Training
IBM Business Partner
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  • Learn on Demand
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 525 USD
  • Date: 7-Feb-2022
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 815 USD
  • Date: 23-Feb-2022
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 815 USD
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Course Description

Overview

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

Objectives

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

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.

Topics

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

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Self-Paced Training Info

Learn at your own pace with anytime, anywhere training

  • Same in-demand topics as instructor-led public and private classes.
  • Standalone learning or supplemental reinforcement.
  • e-Learning content varies by course and technology.
  • View the Self-Paced version of this outline and what is included in the SPVC course.
  • Learn more about e-Learning

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Self-Paced Training Terms & Conditions

THIS IS A SELF-PACED VIRTUAL CLASS. AFTER YOU REGISTER, YOU HAVE 30 DAYS TO COMPLETE THE COURSE.

This is a Self-Paced virtual class; it is intended for students who do not need the support of a classroom instructor. If you feel you would better benefit from having access to a Subject Matter Expert, please enroll in the Instructor-Led version instead. Minimal technical support is provided to address issues with accessing the platform or problems within the lab environment.

Before you enroll, review the system requirements to ensure that your system meets the minimum requirements for this course. AFTER YOU ARE ENROLLED IN THIS COURSE, YOU WILL NOT BE ABLE TO CANCEL YOUR ENROLLMENT. You are billed for the course when you submit the enrollment form. Self-Paced Virtual Classes are non-refundable. Once you purchase a Self-Paced Virtual Class, you will be charged the full price.

After you receive confirmation that you are enrolled, you will be sent further instructions to access your course material and remote labs. A confirmation email will contain your online link, your ID and password, and additional instructions for starting the course.

You can start the course at any time within 12 months of enrolling for the course. After you register/start the course, you have 30 days to complete your course. Within this 30 days, the self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day.

If the course requires a remote lab system, the lab system access is allocated on a first-come, first-served basis. When you are not using the elab system, ensure that you suspend your elab to maximize your hours available to use the elab system.

Click the Skytap Connectivity Test button to ensure this computer's hardware, software and internet connection works with the SPVC Lab Environment.

Click the Skytap Connectivity Documentation button to read about the hardware, software and internet connection requirements.

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