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

Price
1,095 - 1,630 USD
16 Hours
0E079GS
Self-Paced Training
IBM Business Partner

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  • Learn on Demand
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 1,095 USD
  • Guaranteed to Run
    Date: 18-Mar-2024 to 19-Mar-2024
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 1,630 USD
  • Guaranteed to Run
    Date: 15-Apr-2024 to 16-Apr-2024
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 1,630 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 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.

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 machine learning models

• Taxonomy of machine learning models 

• Identify measurement levels 

• Taxonomy of supervised models 

• Build and apply models in IBM SPSS Modeler  

 

Supervised models: Decision trees - CHAID 

• CHAID basics for categorical targets 

• Include categorical and continuous predictors 

• CHAID basics for continuous targets 

• Treatment of missing values  

 

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  

 

Evaluation measures for supervised models 

• Evaluation measures for categorical targets 

• Evaluation measures for continuous targets  

 

Supervised models: Statistical models for continuous targets - Linear regression 

• Linear regression basics 

• Include categorical predictors 

• Treatment of missing values  

 

Supervised models: Statistical models for categorical targets - Logistic regression

• Logistic regression basics 

• Include categorical predictors 

• Treatment of missing values

 

Association models: Sequence detection 

• Sequence detection basics 

• Treatment of missing values

 

Supervised models: Black box models - Neural networks 

• Neural network basics 

• Include categorical and continuous predictors 

• Treatment of missing values  

 

Supervised models: Black box models - Ensemble models 

• Ensemble models basics 

• Improve accuracy and generalizability by boosting and bagging 

• Ensemble the best models  

 

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  

 

Unsupervised models: TwoStep and Anomaly detection 

• TwoStep basics 

• TwoStep assumptions 

• Find the best segmentation model automatically 

• Anomaly detection basics 

• Treatment of missing values  

 

Association models: Apriori 

• Apriori basics 

• Evaluation measures 

• Treatment of missing values

 

Preparing data for modeling 

• Examine the quality of the data 

• Select important predictors 

• Balance the data

Audience

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

Prerequisites

    • Knowledge of your business requirements

Topics

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  

 

Supervised models: Decision trees - CHAID 

• CHAID basics for categorical targets 

• Include categorical and continuous predictors 

• CHAID basics for continuous targets 

• Treatment of missing values  

 

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  

 

Evaluation measures for supervised models 

• Evaluation measures for categorical targets 

• Evaluation measures for continuous targets  

 

Supervised models: Statistical models for continuous targets - Linear regression 

• Linear regression basics 

• Include categorical predictors 

• Treatment of missing values  

 

Supervised models: Statistical models for categorical targets - Logistic regression

• Logistic regression basics 

• Include categorical predictors 

• Treatment of missing values

 

Association models: Sequence detection 

• Sequence detection basics 

• Treatment of missing values

 

Supervised models: Black box models - Neural networks 

• Neural network basics 

• Include categorical and continuous predictors 

• Treatment of missing values  

 

Supervised models: Black box models - Ensemble models 

• Ensemble models basics 

• Improve accuracy and generalizability by boosting and bagging 

• Ensemble the best models  

 

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  

 

Unsupervised models: TwoStep and Anomaly detection 

• TwoStep basics 

• TwoStep assumptions 

• Find the best segmentation model automatically 

• Anomaly detection basics 

• Treatment of missing values  

 

Association models: Apriori 

• Apriori basics 

• Evaluation measures 

• Treatment of missing values

 

Preparing data for modeling 

• Examine the quality of the data 

• Select important predictors 

• Balance the 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 365 DAYS TO ACCESS 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.

Upon receipt of the Order Confirmation Letter which includes your Enrollment Key (Access code); the course begins its twelve (12) month access period. IMPORTANT!!! If your course provides access to a hands-on lab (Virtual Lab Environment), you will have a specific number of days (typically 30 days) on the remote lab platform to complete your hands-on labs. Do not start your lab until you are ready to use your lab time effectively. Time allotted in the virtual lab environment will be indicated once you apply the enrollment key. 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. Note: This does not add additional days to your Lab Environment time frame.

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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