£575.00

Price for this course

1 HOURS

Duration

Classroom IBM

Delivery

Available dates


Mon14Dec 20 TO Mon14Dec 20

Where

Tech Data
The Capitol Building, Oldbury
Bracknell
RG12 8FZ

Code

TR-646322
Mon14Dec 20 TO Mon14Dec 20

Where

Tech Data ILO UK
Connection details will be communicated separately
Instructor Led
Online

Code

TR-646323
Mon14Dec 20 TO Mon14Dec 20

Where

Tech Data
2nd Floor, Broadwall House, 21 Broadwall Street
London
SE1 9PL

Code

TR-646324
Mon01Feb 21 TO Mon01Feb 21

Where

Tech Data ILO UK
Connection details will be communicated separately
Instructor Led
Online

Code

TR-664246
Tue04May 21 TO Tue04May 21

Where

Tech Data ILO UK
Connection details will be communicated separately
Instructor Led
Online

Code

TR-664247

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.

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.

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

Course Outline

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



FAQs

What do I need to bring with me to my public class?

All required learning materials and equipment are provided in the classroom.

 

 

 

 

When do public training course fees have to be paid?

For public training classes payment must be received no later than three business days prior to the first day of class in order to remain in the class and confirm your seat. Failure to provide payment by this date may result in removal from the class, and/or late cancellation fees applied. You can submit payment in the form of a Purchase Order or credit card.

 

 

 

 

On-site (private) Course Pricing:

To find out more about On-site training e-mail us at enablement@agilesolutions.co.uk or call one of our offices.

 

 

 

 

What is the cancellation policy?

Requests for cancellations or date transfers need to be received at least ten (10) business days prior to the event start date in order to receive a full refund. If a cancellation or reschedule request is received less than ten (10) business days before the start date, the penalty of 100% of the cost of the course will be applied, resulting in no amount of the fee being refunded. Refunds will not be allowed for “no-shows” in our public training or IVA courses. This cancellation policy is strictly enforced.

 

 

 

 

What happens if Agile Solutions needs to cancel or reschedule a course?

Agile Solutions reserves the right to cancel events for any reason at any time. Cancellation liability for Agile Solutions, if Agile Solutions cancels the course, is limited to the return of course payment ONLY. Agile Solutions will not reimburse registrants for any other costs including but not limited to any travel cancellation fees or penalties, including airfare and hotel costs. PLEASE NOTE: If your registration status is either “Approved”, or “Pending Payment” you have not been confirmed for the class and it is recommended that you do not make any travel arrangements until you have received a confirmation e-mail letting you know the class and registration is confirmed.

 

 

 

 

How will I know if my course has been rescheduled?

Agile Solutions reserves the right to reschedule or cancel a course due to low enrollment or if necessitated by other circumstances. Agile Solutions will contact you via e-mail or phone to inform you of the change of schedule. Once you have been notified you may reschedule or receive a full credit. Agile Solutions shall not be liable for any other costs including but not limited to any non-refundable travel arrangements if a course is rescheduled or cancelled.