£1,150.00

Price for this course

2 HOURS

Duration

Classroom IBM

Delivery

Available dates


Mon14Dec 20 TO Tue15Dec 20

Where

Tech Data
The Capitol Building, Oldbury
Bracknell
RG12 8FZ

Code

TR-646346
Mon14Dec 20 TO Tue15Dec 20

Where

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

Code

TR-646347
Mon14Dec 20 TO Tue15Dec 20

Where

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

Code

TR-646348
Mon01Mar 21 TO Tue02Mar 21

Where

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

Code

TR-664254
Tue01Jun 21 TO Wed02Jun 21

Where

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

Code

TR-664255

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.

Audience

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

Prerequisites

  • Knowledge of your business requirements

Objective

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

Course Outline

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

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

Association models: Sequence detection
• Sequence detection basics
• Treatment of missing values

Preparing data for modeling
• Examine the quality of the data
• Select important predictors
• Balance the 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.