INTRODUCTION

IBM SPSS modeler is a statistical platform with versatility when it comes to data analytics. It comes with a wide range of uses such as automatic data preparation ,visual analysis streams, text analytics, a variety of algorithmic methods and many more.

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. It also contains PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

 Duration

5 days

Who Should Attend

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

COURSE LEVEL: Advanced

Course Objectives

By the end of the training, participants should be able to;

• Build and apply models using IBM SPSS modeler
• Carry out predictive analysis, model management and deployment and machine learning to monetize data assets
• Tap  into data assets and modern applications, with complete algorithms and models

Course Outline

Module 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 

Module 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 

Module 3

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 

Module 4

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

Module 5

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 

Module 6

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: Two-Step and Anomaly detection 
• Two-Step basics 
• Two-Step assumptions 
• Find the best segmentation model automatically 
• Anomaly detection basics 
• Treatment of missing values 

Module 7

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 Administration Details:

METHODOLOGY

The instructor-led training is delivered using a blended learning approach and comprises of presentations, guided sessions of practical exercise, web-based tutorials and group work. Our facilitators are seasoned industry experts with years of experience, working as professional and trainers in these fields.

All facilitation and course materials will be offered in English. The participants should be reasonably proficient in English.

ACCREDITATION

Upon successful completion of this training, participants will be issued with an Indepth Research Institute (IRES) certificate certified by the National Industrial Training Authority (NITA).

TRAINING VENUE

The training will be held at IRES Training Centre. The course fee covers the course tuition, training materials, two break refreshments and lunch.

All participants will additionally cater for their, travel expenses, visa application, insurance, and other personal expenses.

ACCOMMODATION AND AIRPORT PICKUP

Accommodation and airport pickup are arranged upon request. For reservations contact the Training Officer.

Email:[email protected].  

Mob: +254 715 077 817

TAILOR- MADE

This training can also be customized to suit the needs of your institution upon request. You can have it delivered in our IRES Training Centre or at a convenient location.

For further inquiries, please contact us on Tel: +254 715 077 817.

Mob: +254 792516000, +254 792516010 or mail [email protected]

PAYMENT

Payment should be transferred to IRES account through bank before the course start date

Send proof of payment to [email protected]

DOWNLOADABLE DOCUMENTS: