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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.
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What You'll Learn

Please refer to course overview

Who Should Attend

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

Knowledge of your business requirements

Learning Journey

Coming Soon...

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 - CR Tree

  • CR Tree basics for categorical targets
  • Include categorical and continuous predictors
  • CR 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
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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Course Curriculum

Course Curriculum

Training Schedule

Training Schedule

Exam & Certification

Exam & Certification

FAQs

Frequently Asked Questions

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