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Overview

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.
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What You'll Learn

Please refer to course overview.

Who Should Attend

  • Data scientists
  • Business analysts
  • Experienced users of IBM SPSS Modeler who want to learn about advanced techniques in the software
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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.

Learning Journey

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1. Introduction to advanced machine learning models

  • Taxonomy of model
  • 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

This course is not associated with any Certification.

Frequently Asked Questions (FAQs)

  • Why get IBM certified?

    IBM certifications validate your skills and expertise in a wide range of technologies, including hybrid cloud, data & AI, security, IT infrastructure, and business applications.

    These certifications are globally recognized and can help you advance your career, increase your earning potential, and demonstrate your commitment to professional development.

    IBM-certified professionals are in high demand, making them valuable assets to any organization seeking to leverage IBM's innovative solutions.

  • What to expect for the examination?

    IBM offers a variety of certification exams at different levels (Basic, Intermediate, and Advanced) covering various technologies and job roles.

    Exams typically consist of multiple-choice questions and may include scenario-based questions that assess your ability to apply your knowledge in real-world situations.

    Note: Certification requirements and policies may be updated by IBM from time to time. We apologize for any discrepancies; do get in touch with us if you have any questions.

  • How long is IBM certification valid for?

    The validity period of IBM certifications varies. Some certifications, particularly those focused on specific product versions or technologies, may have expiration dates.

    However, many IBM certifications, especially those focused on broader skills or newer technologies, do not have an expiration date.

    Note: Certification requirements and policies may be updated by IBM from time to time. We apologize for any discrepancies; do get in touch with us if you have any questions.

  • Why take this course with Trainocate?

    Here’s what sets us apart:

    - Global Reach, Localized Accessibility: Benefit from our geographically diverse training hubs in 16 countries (and counting!).

    - Top-Rated Instructors: Our team of subject matter experts (with high average CSAT and MTM scores) are passionate to help you accelerate your digital transformation.

    - Customized Training Solutions: Choose from on-site, virtual classrooms, or self-paced learning to fit your organization and individual needs.

    - Experiential Learning: Dive into interactive training with our curated lesson plans. Participate in hands-on labs, solve real-world challenges, and take on comprehensive assessments.

    - Learn From The Best: With 30+ authorized training partnerships and countless awards from Microsoft, AWS, Google – you're guaranteed learning from the industry's elite.

    - Your Bridge To Success: We provide up-to-date course materials, helpful exam guides, and dedicated support to validate your expertise and elevate your career.

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