Learn Jube, R, Netica and Exhaustive.
Real World Use Cases without the Math
A training course to implement analytical techniques in a real business environment cutting through the academic theory to get straight to outcomes. Each case study is based on the real world experience of the trainer and Jube.
$1200 Free Cloud Credit or Free Trial, then 10% Off Server Licencing
The course includes a $1200 Jube Platform credit valid for six months.
The course includes six months Server trial licence then 10% off a Server licence valid for six months.
The training will provide the attendee with the ability to administer and maintain the Jube platform and make use of machine learning.
The course will present a variety of use cases to showcase all functionality available in the Jube platform and the machine learning package integrations. The use cases to be explored in the course are:
Fraud Prevention in eCommerce, Debit Card and Risk Based Authentication.
Stock Market Numeric Prediction.
Credit Lending Risk.
Instruction Detection via Packet Sniffing and Syslog Monitoring.
Jube integrates several machine learning packages and a variety of machine learning techniques will be explored:
Linear Regression in R.
Logistic Regression in R.
Decision Trees in R.
Neural Networks in R.
Logistic Regression and Neural Networks in Exhaustive.
Bayesian Networks in Netica.
The Training Course
Training course participants are required to bring their own laptop. The training course includes:
$1200 Free Jube Platform Credit on completion.
Four, optionally five, days of hands-on, in person training.
A remote Windows Desktop with all required predictive analytics training course software and datasets installed in advance.
Guaranteed small class size. The course is confirmed with a minimum of two participants and sealed at a maximum of eight participants. With such small class sizes there will be plenty of time to ask questions and receive personal attention from the trainer.
A highly consultative engagement. There will be plenty of time to discuss your specific projects and learning objective to provide immediate return to your organisation upon course completion.
In Person Training
The venue is announced not less than two weeks prior to course commencement. Participants are required to bring their own laptops.
Can’t Travel? Participate via Virtual Classroom
The same In Person Training is also available via Virtual Training Room . The Virtual Training Room enables remote attendees to experience the benefits of instructor-led training without having to travel. Remote participants experience the same collaboration, instructor interaction, and learning benefits as those who are physically in the classroom. Virtual Training Room technology allows all students to:
See and hear everything that is taking place in the live classroom
Raise their hands and ask questions
View and interact with course content (presentations, videos, whiteboard notes and more)
Experience real-time collaboration
All remote users need to participate in a Virtual Training Room event is a computer with a camera, wired* internet connection, speakers, and a microphone — it’s that easy. Wired internet connection is suggested for stronger connection and reliability; wireless connection is also allowed.
The course agenda is as follows:
Training Day 1
Get to grips with the Jube Platform concepts:
Module 1: Methodology and Platform Introduction.
Case Study 1: Platform Acclimatization.
Module 2: Messaging and Processing Introduction.
Case Study 2: Messaging the Jube Platform.
Module 3: Introduction to the Entity Model System.
Case Study 3: Financial Transaction Model, Payload Definition and Inline Scripting.
Module 4: Entity Abstractions, Abstraction Calculations, Abstraction Deviations and Search Key Cache.
Case Study 4: Creating Fraud Prevention Abstractions.
Module 5: Sampling Activation, Response Elevations, TTL Counters and Evaluations.
Case Study 5: TTL Counter Activation.
Module 6: Case Management.
Case Study 6: Fraud Prevention Alerts.
Training Day 2
More Jube Platform concepts and an introduction to R machine learning:
Module 7: The Symbol Registry, Symbol Models and Symbol Covariance.
Case Study 7: Consuming Stock Prices.
Module 8: Symbol Abstraction, Abstraction Deviation, Activation and Evaluation.
Case Study 8: Emulating Technical Analysis of a Chart.
Module 9: Introduction to Adaptations and Data Extraction Jobs.
Module 10: Getting Started with R.
Module 11: Data Structures in R.
Module 12: Summary Statistics and Basic Plots in R.
Module 13: Linear Regression in R.
Case Study 13: Stock Price Forecasting with Linear Regression.
Training Day 3
More R machine learning and an introduction to Netica machine learning:
Module 14: Logistic Regression in R.
Case Study 14: Fraud Prevention with Logistic Regression.
Module 15: Decision Trees in R.
Case Study 15: Decision Trees for Credit Risk Analysis.
Module 16: Norsys Netica, Bayesian Analysis and Prescriptive Analytics.
Case Study 16: Credit Risk Analysis in Norsys Netica.
Training Day 4
Putting it all into practice:
Module 17: R Naive Bayesian Classifiers and Laplace Estimator.
Module 18: Neural Networks in R.
Module 19: Exhaustive Search for Regression and Neural Network Topology Exploration.
Module 20: Syslog and PropSniff integration for network and host intrusion detection.
Case Study 20: Block IP on Failed Login, Abuse IP, Excessive Ping or SQL injection.
Module 21: Security, Backup and Recovery and Consolidation.
Module 22: Getting Help from Jube, Backup and Recovery of Tenants.
Training Day 5 (Optional)
Installing the Jube Platform on your own servers and performing advanced configuration and reporting:
Module 23: Platform Technical Architecture.
Case Study 23: Installing the Prerequisites and the Jube Platform.
Module 24: Databases, Compression and Encryption Zones.
Module 25: Telerik Reporting Designer.
Case Study 25: Creating Reports in Jube and Telerik Reporting Designer.
Module 26: Entity Model Inheritance, Volume Monitoring and Background Limits.
Case Study 26: Create a Real Time Auction for Advertising Technology.
Module 27: .net platform augmentation.
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