Classroom
30000.00
ILO
22000.00
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:
Before taking this course, make sure that you have the following skills:
Please Refer Objective
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.
Knowledge of your business requirements
Introduction to machine learning models
Supervised models: Decision trees - CHAID
Supervised models: Decision trees - C&R Tree
Evaluation measures for supervised models
Supervised models: Statistical models for continuous targets - Linear regression
Supervised models: Statistical models for categorical targets - Logistic regression
Association models: Sequence detection
Supervised models: Black box models - Neural networks
Supervised models: Black box models - Ensemble models
Unsupervised models: K-Means and Kohonen
Unsupervised models: TwoStep and Anomaly detection
Association models: Apriori
Preparing data for modeling
Introduction to machine learning models
Supervised models: Decision trees - CHAID
Supervised models: Decision trees - C&R Tree
Evaluation measures for supervised models
Supervised models: Statistical models for continuous targets - Linear regression
Supervised models: Statistical models for categorical targets - Logistic regression
Supervised models: Black box models - Neural networks
Supervised models: Black box models - Ensemble models
Unsupervised models: K-Means and Kohonen
Unsupervised models: TwoStep and Anomaly detection
Association models: Apriori
Association models: Sequence detection
Preparing data for modeling
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.
It is recommended that you have an understanding of your business data
Please refer to course overview
1. Introduction to data science
2. Introduction to IBM SPSS Modeler
3. Introduction to data science using IBM SPSS Modeler
4. Collecting initial data
5. Understanding the data
6. Setting the of analysis
7. Integrating data
8. Deriving and reclassifying fields
9. Identifying relationships
10. Introduction to modeling
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.
1: Introduction to clustering and association modeling
2: Clustering models and K-Means clustering
3: Clustering using the Kohonen network
4: Clustering using TwoStep clustering
5: Use Apriori to generate association rules
6: Use advanced options in Apriori
7: Sequence detection
8: Advanced Sequence detection
A: Examine learning rate in Kohonen networks (Optional)
B: Association using the Carma model (Optional)
1: Introduction to clustering and association modeling
2: Clustering models and K-Means clustering
3: Clustering using the Kohonen network
4: Clustering using TwoStep clustering
5: Use Apriori to generate association rules
6: Use advanced options in Apriori
7: Sequence detection
8: Advanced Sequence detection
A: Examine learning rate in Kohonen networks (Optional)
B: Association using the Carma model (Optional)
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.
Please refer to course overview
Please Refer Objective
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.
Introduction to advanced machine learning models
Group fields: Factor Analysis and Principal Component Analysis
Predict targets with Nearest Neighbor Analysis
Explore advanced supervised models
Introduction to Generalized Linear Models
Combine supervised models
Use external machine learning models
Analyze text data
Introduction to advanced machine learning models
Group fields: Factor Analysis and Principal Component Analysis
Predict targets with Nearest Neighbor Analysis
Explore advanced supervised models
Introduction to Generalized Linear Models
Combine supervised models
Use external machine learning models
Analyze text data
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.
1: Introduction to predictive models for categorical targets
2: Building decision trees interactively with CHAID
3: Building decision trees interactively with C&R Tree and Quest
4: Building decision trees directly
5: Using traditional statistical models
6: Using machine learning models
1: Introduction to predictive models for categorical targets
2: Building decision trees interactively with CHAID
3: Building decision trees interactively with C&R Tree and Quest
4: Building decision trees directly
5: Using traditional statistical models
6: Using machine learning models
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.
Please refer to course overview
1: Using functions to cleanse and enrich data
2: Using additional field transformations
3: Working with sequence data
4: Sampling, partitioning and balancing data
5: Improving efficiency
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.
1: Introduction to predictive models for continuous targets
2: Building decision trees interactively
3: Building decision trees directly
4. Using traditional statistical models
5: Using machine learning models
1: Introduction to predicting continuous targets
2: Building decision trees interactively
3: Building your tree directly
4: Using traditional statistical models
5: Using machine learning models
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.
IBM BigInsights 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:
Please refer to course overview
1: Using functions to cleanse and enrich data
2: Using additional field transformations
3: Working with sequence data
4: Sampling, partitioning and balancing data
5: Improving efficiency