Machine Learning

The main goal of this course is to familiarize you with all aspects of Machine Learning so that you can start your career as an Machine Learning Engineer.

What you will learn

  • Training machines using data
  • Representation of artificial neural networks
  • Perform linear regression on multiple variables using Python
  • Categorize data by Python using Logistic regression
  • Use a vector machine algorithm
  • Designing machine learning systems
  • Principal component analysis of data modeling, etc.

Classroom

30000.00

ILO

22000.00

Audience

  • Professionals working in the field of Data Science, Analytics, BI, Search Engine, and E-commerce domains
  • Professionals seeking a career change
  • Undergraduates and freshers

Pre-requisites

  • Programming knowledge on Python

IBM

Table of Content

1.Course Overview

  • You will have an overview of IBM's big data strategy and review why it is important to understand and use big data. It will cover IBM BigInsights as a platform for managing and gaining insights from your big data. As such, you will see how the BigInsights have aligned their offerings to better suit your needs with the IBM Open Platform (IOP) along with the three specialized modules with value-add that sits on top of the IOP. You will also get an introduction to the BigInsights value-add including Big SQL, BigSheets, and Big R. The participant will be engaged with the product through interactive exercises.

    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.

2.Pre-requisties

  • None, however, knowledge of Linux would be beneficial.

3.Objective

  • Understand the purpose of big data and know why it is important
  • List the sources of data (data-at-rest vs data-in-motion)
  • Describe the IBM BigInsights offering
  • Utilize the various IBM BigInsights tools including Big SQL, BigSheets, Big R, Jaql and AQL for your big data needs.
  • IBM Open Platform (IOP) with Apache Hadoop

4.Course Outline

  • IBM BigInsights Overview:

  • Introduction to Big Data
  • Introduction to IBM BigInsights
  • IBM BigInsights for Analysts
  • IBM BigInsights for Data Scientist
  • IBM BigInsights for Enterprise Management

Table of Content

1.Course Overview

This IBM SPSS Modeler Training course provides the fundamentals of using IBM SPSS Modeler and introduces the participant to data science. The principles and practice of data science are illustrated using the CRISP-DM methodology. The Data Science Training course provides training in the basics of how to import, explore, and prepare data with IBM SPSS Modeler v18.1.1, and introduces the student to modeling.

2.Pre-requisties

It is recommended that you have an understanding of your business data

3.Objective

Please refer to course overview

4.Course Outline

1. Introduction to data science

  • List two applications of data science
  • Explain the stages in the CRISP-DM methodology
  • Describe the skills needed for data science

 

2. Introduction to IBM SPSS Modeler

  • Describe IBM SPSS Modeler's user-interface
  • Work with nodes and streams
  • Generate nodes from output
  • Use SuperNodes
  • Execute streams
  • Open and save streams
  • Use Help

 

3. Introduction to data science using IBM SPSS Modeler

  • Explain the basic framework of a data-science project
  • Build a model
  • Deploy a model

 

4. Collecting initial data

  • Explain the concepts "data structure", "of analysis", "field storage" and "field measurement level"
  • Import Microsoft Excel files
  • Import IBM SPSS Statistics files
  • Import text files
  • Import from databases
  • Export data to various formats

 

5. Understanding the data

  • Audit the data
  • Check for invalid values
  • Take action for invalid values
  • Define blanks

 

6. Setting the of analysis

  • Remove duplicate records
  • Aggregate records
  • Expand a categorical field into a series of flag fields
  • Transpose data

 

7. Integrating data

  • Append records from multiple datasets
  • Merge fields from multiple datasets
  • Sample records

 

8. Deriving and reclassifying fields

  • Use the Control Language for Expression Manipulation (CLEM)
  • Derive new fields
  • Reclassify field values

 

9. Identifying relationships

  • Examine the relationship between two categorical fields
  • Examine the relationship between a categorical field and a continuous field
  • Examine the relationship between two continuous fields

 

10. Introduction to modeling

  • List three types of models
  • Use a supervised model
  • Use a segmentation model

 

Table of Content

1.Course Overview

This Time Series Analysis using SPSS course covers advanced topics to aid in the preparation of data for a successful data science project. In this Time Series Analysis Training You will learn how to use functions, deal with missing values, use advanced field operations, handle sequence data, apply advanced sampling methods, and improve efficiency.

Learning Journeys that reference this course:

2.Pre-requisties

  • Experience using IBM SPSS Modeler including familiarity with the Modeler environment, creating streams, reading data files, exploring data, setting the unit of analysis, combining datasets, deriving and reclassifying fields, and basic knowledge of modeling.
  • Prior completion of the Introduction to IBM SPSS Modeler and Data Science course is recommended.

3.Objective

Please refer to course overview

4.Course Outline

1: Using functions to cleanse and enrich data

  • Use date functions
  • Use conversion functions
  • Use string functions
  • Use statistical functions
  • Use missing value functions

 

2: Using additional field transformations

  • Replace values with the Filler node
  • Recode continuous fields with the Binning node
  • Change a field’s distribution with the Transform node

 

3: Working with sequence data

  • Use sequence functions
  • Count an event across records
  • Expand a continuous field into a series of continuous fields with the Restructure node
  • Use geospatial and time data with the Space-Time-Boxes node

 

4: Sampling, partitioning and balancing data

  • Draw simple and complex samples with the Sample node
  • Create a training set and testing set with the Partition node
  • Reduce or boost the number of records with the Balance node

 

5: Improving efficiency

  • Use database scalability by SQL pushback
  • Process outliers and missing values with the Data Audit node
  • Use the Set Globals node
  • Use parameters
  • Use looping and conditional execution

 

Table of Content

1.Course Overview

Are you getting ready to administer database security policies? Learn how to configure Guardium V10 to discover, classify, analyze, protect, and control access to sensitive data. You will learn to perform vulnerability assessment, and how to monitor data and file activity. This course also teaches you how to create reports, audits, alerts, metrics, and compliance oversight processes.

Learning Journeys that reference this course:

2.Pre-requisties

Before taking this course, make sure that you have the following skills:

  • Working knowledge of SQL queries for IBM DB2 and other databases
  • Working knowledge of UNIX commands
  • Familiarity with data protection standards such as HIPAA and CPI

3.Objective

  • Identify the primary functions of IBM Guardium
  • Apply key Guardium architecture components
  • Navigate the Guardium user interface and command line interface
  • Manage user access to Guardium
  • Use the administration console to manage Guardium components
  • Build and populate Guardium groups
  • Configure policy rules that process the information gathered from database and file servers
  • Use the configuration auditing system, Vulnerability Assessment application, and Database Discovery to perform data security tasks
  • Create queries and reports to examine trends and gather data
  • Automate compliance workflow processes
  • Use file acess monitoring to keep track of the files on your servers

4.Course Outline

Please Refer Objective

 

Table of Content

1.Course Overview

This Machine Learning in SPSS 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.

2.Pre-requisties

  • 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.

3.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

 

4.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

 

Table of Content

1.Course Overview

Clustering and Association Modeling Using IBM SPSS Modeler (v18.1.1) introduces modelers to two specific classes of modeling that are available in IBM SPSS Modeler: clustering and associations. Participants will explore various clustering techniques that are often employed in market segmentation studies. Participants will also explore how to create association models to find rules describing the relationships among a set of items, and how to create sequence models to find rules describing the relationships over time among a set of items.

2.Pre-requisties

  • Experience using IBM SPSS Modeler
  • A familiarity with the IBM SPSS Modeler environment: creating models, creating streams, reading in data files, and assessing data quality
  • A familiarity with handling missing data (including Type and Data Audit nodes), and basic data manipulation (including Derive and Select nodes)

3.Objective

1: Introduction to clustering and association modeling

  • Identify the association and clustering modeling techniques available in IBM SPSS Modeler
  • Explore the association and clustering modeling techniques available in IBM SPSS Modeler
  • Discuss when to use a particular technique on what type of data

 

2: Clustering models and K-Means clustering

  • Identify basic clustering models in IBM SPSS Modeler
  • Identify the basic characteristics of cluster analysis
  • Recognize cluster validation techniques
  • Understand K-Means clustering principles
  • Identify the configuration of the K-means node

 

3: Clustering using the Kohonen network

  • Identify the basic characteristics of the Kohonen network
  • Understand how to configure a Kohonen node
  • Model a Kohonen network

 

4: Clustering using TwoStep clustering

  • Identify the basic characteristics of TwoStep clustering
  • Identify the basic characteristics of TwoStep-AS clustering
  • Model and analyze a TwoStep clustering solution

 

5: Use Apriori to generate association rules

  • Identify three methods of generating association rules
  • Use the Apriori node to build a set of association rules
  • Interpret association rules

 

6: Use advanced options in Apriori

  • Identify association modeling terms and rules
  • Identify evaluation measures used in association modeling
  • Identify the capabilities of the Association Rules node
  • Model associations and generate rules using Apriori

 

7: Sequence detection

  • Explore sequence detection association models
  • Identify sequence detection methods
  • Examine the Sequence node
  • Interpret the sequence rules and add sequence predictions to steams

 

8: Advanced Sequence detection

  • Identify advanced sequence detection options used with the Sequence node
  • Perform in-depth sequence analysis
  • Identify the expert options in the Sequence node
  • Search for sequences in Web log data

 

A: Examine learning rate in Kohonen networks (Optional)

  • Understand how a Kohonen neural network learns

 

B: Association using the Carma model (Optional)

  • Review association rules
  • Identify the Carma model
  • Identify the Carma node
  • Model associations and generate rules using Carma

 

4.Course Outline

1: Introduction to clustering and association modeling

  • Identify the association and clustering modeling techniques available in IBM SPSS Modeler
  • Explore the association and clustering modeling techniques available in IBM SPSS Modeler
  • Discuss when to use a particular technique on what type of data

 

2: Clustering models and K-Means clustering

  • Identify basic clustering models in IBM SPSS Modeler
  • Identify the basic characteristics of cluster analysis
  • Recognize cluster validation techniques
  • Understand K-Means clustering principles
  • Identify the configuration of the K-means node

 

3: Clustering using the Kohonen network

  • Identify the basic characteristics of the Kohonen network
  • Understand how to configure a Kohonen node
  • Model a Kohonen network

 

4: Clustering using TwoStep clustering

  • Identify the basic characteristics of TwoStep clustering
  • Identify the basic characteristics of Two Step AS clustering
  • Model and analyze a TwoStep clustering solution

 

5: Use Apriori to generate association rules

  • Identify three methods of generating association rules
  • Use the Apriori node to build a set of association rules
  • Interpret association rules

 

6: Use advanced options in Apriori

  • Identify association modeling terms and rules
  • Identify evaluation measures used in association modeling
  • Identify the capabilities of the Association Rules node
  • Model associations and generate rules using Apriori

 

7: Sequence detection

  • Explore sequence detection association models
  • Identify sequence detection methods
  • Examine the Sequence node
  • Interpret the sequence rules and add sequence predictions to steams

 

8: Advanced Sequence detection

  • Identify advanced sequence detection options used with the Sequence node
  • Perform in-depth sequence analysis
  • Identify the expert options in the Sequence node
  • Search for sequences in Web log data

 

A: Examine learning rate in Kohonen networks (Optional)

  • Understand how a Kohonen neural network learns

 

B: Association using the Carma model (Optional)

  • Review association rules
  • Identify the Carma model
  • Identify the Carma node
  • Model associations and generate rules using Carma

 

Table of Content

1.Course Overview

This IBM SPSS Data Preparation course covers advanced topics to aid in the preparation of data for a successful data science project. In this IBM SPSS Training You will learn how to use functions, deal with missing values, use advanced field operations, handle sequence data, apply advanced sampling methods, and improve efficiency.

2.Pre-requisties

  • Experience using IBM SPSS Modeler including familiarity with the Modeler environment, creating streams, reading data files, exploring data, setting the unit of analysis, combining datasets, deriving and reclassifying fields, and basic knowledge of modeling.
  • Prior completion of the Introduction to IBM SPSS Modeler and Data Science course is recommended.

3.Objective

Please refer to course overview

4.Course Outline

1: Using functions to cleanse and enrich data

  • Use date functions
  • Use conversion functions
  • Use string functions
  • Use statistical functions
  • Use missing value functions

 

2: Using additional field transformations

  • Replace values with the Filler node
  • Recode continuous fields with the Binning node
  • Change a field’s distribution with the Transform node

 

3: Working with sequence data

  • Use sequence functions
  • Count an event across records
  • Expand a continuous field into a series of continuous fields with the Restructure node
  • Use geospatial and time data with the Space-Time-Boxes node

 

4: Sampling, partitioning and balancing data

  • Draw simple and complex samples with the Sample node
  • Create a training set and testing set with the Partition node
  • Reduce or boost the number of records with the Balance node

 

5: Improving efficiency

  • Use database scalability by SQL pushback
  • Process outliers and missing values with the Data Audit node
  • Use the Set Globals node
  • Use parameters
  • Use looping and conditional execution

 

Table of Content

1.Course Overview

This SPSS Machine Learning course provides an introduction to supervised models, unsupervised models, and association models. This Course in Machine Learning 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.

2.Pre-requisties

Knowledge of your business requirements

3.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

4.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

 

Table of Content

1.Course Overview

This SPSS Predictive Modeling course focuses on using analytical models to predict a categorical field, such as churn, fraud, response to a mailing, pass/fail exams, and machine break-down. Students are introduced to decision trees such as CHAID and C&R Tree, traditional statistical models such as Logistic Regression, and machine learning models such as Neural Networks. In this SPSS Modeler Course Students will learn about important options in dialog boxes, how to interpret the results, and explain the major differences between the models.

2.Pre-requisties

  • Experience using IBM SPSS Modeler including familiarity with the Modeler environment, creating streams, reading data files, exploring data, setting the unit of analysis, combining datasets, deriving and reclassifying fields, and a basic knowledge of modeling.
  • Prior completion of Introduction to IBM SPSS Modeler and Data Science (v18.1) is recommended.

3.Objective

1: Introduction to predictive models for categorical targets

  • Identify three modeling objectives
  • Explain the concept of field measurement level and its implications for selecting a modeling technique
  • List three types of models to predict categorical targets

 

2: Building decision trees interactively with CHAID

  • Explain how CHAID grows decision trees
  • Build a customized model with CHAID
  • Evaluate a model by means of accuracy, risk, response and gain
  • Use the model nugget to score records

 

3: Building decision trees interactively with C&R Tree and Quest

  • Explain how C&R Tree grows a tree
  • Explain how Quest grows a tree
  • Build a customized model using C&R Tree and Quest
  • List two differences between CHAID, C&R Tree, and Quest

 

4: Building decision trees directly

  • Customize two options in the CHAID node
  • Customize two options in the C&R Tree node
  • Customize two options in the Quest node
  • Customize two options in the C5.0 node
  • Use the Analysis node and Evaluation node to evaluate and compare models
  • List two differences between CHAID, C&R Tree, Quest, and C5.0

 

5: Using traditional statistical models

  • Explain key concepts for Discriminant
  • Customize one option in the Discriminant node
  • Explain key concepts for Logistic
  • Customize one option in the Logistic node

 

6: Using machine learning models

  • Explain key concepts for Neural Net
  • Customize one option in the Neural Net node

 

4.Course Outline

1: Introduction to predictive models for categorical targets

  • Identify three modeling objectives
  • Explain the concept of field measurement level and its implications for selecting a modeling technique
  • List three types of models to predict categorical targets

 

2: Building decision trees interactively with CHAID

  • Explain how CHAID grows decision trees
  • Build a customized model with CHAID
  • Evaluate a model by means of accuracy, risk, response and gain
  • Use the model nugget to score records

 

3: Building decision trees interactively with C&R Tree and Quest

  • Explain how C&R Tree grows a tree
  • Explain how Quest grows a tree
  • Build a customized model using C&R Tree and Quest
  • List two differences between CHAID, C&R Tree, and Quest

 

4: Building decision trees directly

  • Customize two options in the CHAID node
  • Customize two options in the C&R Tree node
  • Customize two options in the Quest node
  • Customize two options in the C5.0 node
  • Use the Analysis node and Evaluation node to evaluate and compare models
  • List two differences between CHAID, C&R Tree, Quest, and C5.0

 

5: Using traditional statistical models

  • Explain key concepts for Discriminant
  • Customize one option in the Discriminant node
  • Explain key concepts for Logistic
  • Customize one option in the Logistic node

 

6: Using machine learning models

  • Explain key concepts for Neural Net
  • Customize one option in the Neural Net node

 

Table of Content

1.Course Overview

This Predictive Modeling Online course provides an overview of how to use IBM SPSS Modeler to predict a target field that describes numeric values. Students will be exposed to rule induction models such as CHAID and C&R Tree. They will also be introduced to traditional statistical models such as Linear Regression. In this SPSS Online Course Students are introduced to machine learning models, such as Neural Networks. Business use case examples include: predicting the length of subscription for newspapers, telecommunication, and job length, as well as predicting insurance claim amounts.

2.Pre-requisties

  • Experience using IBM SPSS Modeler including familiarity with the Modeler environment, creating streams, reading data files, exploring data, setting the unit of analysis, combining datasets, deriving and reclassifying fields, and a basic knowledge of modeling.
  • Prior completion of Introduction to IBM SPSS Modeler and Data Science (v18.1.1) is recommended.

3.Objective

1: Introduction to predictive models for continuous targets

  • List three modeling objectives
  • List two business questions that involve predicting continuous targets
  • Explain the concept of field measurement level and its implications for selecting a modeling technique
  • List three types of models to predict continuous targets
  • Determine the classification model to use

 

2: Building decision trees interactively

  • Explain how CHAID grows a tree
  • Explain how C&R Tree grows a tree
  • Build CHAID and C&R Tree models interactively
  • Evaluate models for continuous targets
  • Use the model nugget to score records

 

3: Building decision trees directly

  • Customize two options in the CHAID node
  • Customize two options in the C&R Tree node
  • List one difference between CHAID and C&R Tree

 

4. Using traditional statistical models

  • Explain key concepts for Linear
  • Customize options in the Linear node
  • Explain key concepts for Cox
  • Customize options in the Cox node

 

5: Using machine learning models

  • Explain key concepts for Neural Net
  • Customize one option in the Neural Net node

4.Course Outline

1: Introduction to predicting continuous targets

  • List three modeling objectives
  • List two business questions that involve predicting continuous targets
  • Explain the concept of field measurement level and its implications for selecting a modeling technique
  • List three types of models to predict continuous targets
  • Determine the classification model to use

 

2: Building decision trees interactively

  • Explain how CHAID grows a tree
  • Explain how C&R Tree grows a tree
  • Build CHAID and C&R Tree models interactively
  • Evaluate models for continuous targets
  • Use the model nugget to score records

 

3: Building your tree directly

  • Explain the difference between CHAID and Exhaustive CHAID
  • Explain boosting and bagging
  • Identify how C&R Tree prunes decision trees
  • List two differences between CHAID and C&R Tree

 

4: Using traditional statistical models

  • Explain key concepts for Linear
  • Customize options in the Linear node
  • Explain key concepts for Cox
  • Customize options in the Cox node

 

5: Using machine learning models

  • Explain key concepts for Neural Net
  • Customize one option in the Neural Net node

 

Table of Content

1.Course Overview

Clustering and Association Modeling Using IBM SPSS Modeler (v18.1.1) introduces modelers to two specific classes of modeling that are available in IBM SPSS Modeler: clustering and associations. Participants will explore various clustering techniques that are often employed in market segmentation studies. Participants will also explore how to create association models to find rules describing the relationships among a set of items, and how to create sequence models to find rules describing the relationships over time among a set of items.

2.Pre-requisties

  • Experience using IBM SPSS Modeler
  • A familiarity with the IBM SPSS Modeler environment: creating models, creating streams, reading in data files, and assessing data quality
  • A familiarity with handling missing data (including Type and Data Audit nodes), and basic data manipulation (including Derive and Select nodes)

3.Objective

Please refer to course overview

4.Course Outline

Please Refer Objective