• 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)

Please refer to course overview.

Please Refer Course Objective.

• 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.

Please refer to course overview.

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

For any further queries, feel free to contact the top technical training institute bangalore - Global Knowledge Technologies.

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

• 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

Please Refer Course Objective.

• 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.

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

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

For any further queries, feel free to contact the top software training institute in bangalore - Global Knowledge Technologies.

• 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)

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

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

Benefit your career from the best software training in bangalore. For any further queries, feel free to contact Global Knowledge Technologies.

• 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.

Please refer to course overview.

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

For any further queries, feel free to contact the best institute for IT training in bangalore - Global Knowledge Technologies.

Knowledge of your business requirements.

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

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

For any further queries, feel free to contact the best industrial training institute bangalore - Global Knowledge Technologies.

• 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.

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

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

For any further queries on corporate training courses in bangalore, feel free to contact Global Knowledge Technologies.

• 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.

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

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

For any further queries on trending software training courses in bangalore, feel free to contact Global Knowledge Technologies.

None, however, knowledge of Linux would be beneficial.

• 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

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

For any further queries, feel free to contact the best industrial training institute bangalore - Global Knowledge Technologies.