Learn the Jube, R, Exhaustive and Netica in Three Days of Live Training

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 Pragmatic Predictive Analytics.

Pragmatic Predictive Analytics.

Real World Use Cases Provided

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.

 Immediate Return on Training.

Immediate Return on Training.

$1000 Free Jube Platform Credit

The course includes a $1000 Jube Platform credit to encourage participants to immediately apply their newly acquired knowledge.

In the case of R and Netica, these technologies can be used independently of the Jube Platform. R is free and Netica has a free edition available.

Introduction

The training will provide the attendee with the ability to administer and maintain the Jube platform and make use of machine learning package integrations (i.e. R, Netica and Exhaustive). 

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.

  • Credit Risk in Consumer Credit Lending.

  • Stock Market Numeric Prediction.

  • Instruction Detection via Packet Sniffing and Syslog Monitoring.

Jube integrates several machine learning packages and in addition to training the Jube platform, a variety of machine learning techniques will be explored:

  • Logistic Regression 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:

  1. $1000 Free Jube Platform Credit on completion.

  2. Recordings of the training session available for download on completion.

  3. Three days of hands-on, live online training.

  4. A remote Windows Desktop with all required predictive analytics training course software and datasets installed in advance.

  5. Guaranteed small class size. The course is confirmed with a minimum of one participant 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.

  6. 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.

Training Day 1

The first day of the course will explain how to use the Jube platform for creating classification models:

  • 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.

  • Summing Up.

Training Day 2

The second day of the course will explain how to use the Jube platform to create numeric prediction models as well as more advanced classification models. A foundation in R will be provided to underpin the machine learning principles to be delivered on the third day of training:

  • 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: Syslog and PropSniff integration for network and host intrusion detection.

  • Case Study 10:  Block IP on Failed Login, Abuse IP, Excessive Ping or SQL injection.

  • Module 11: Getting Started with R.

  • Module 12: Data Structures in R.

  • Summing Up.

Training Day 3

The third day of the training will introduce the most commonly used machine learning packages supported by the Jube platform. The training course will conclude with a final review of the Jube platform:

  • Module 13: Logistic Regression

  • Case Study 13:  Fraud Prevention in Logistic Regression.

  • Module 14: Norsys Netica, Bayesian Analysis and Prescriptive Analytics.

  • Case Study 14: Credit Risk Analysis in Bayesian Networks.

  • Module 15: Neural Networks.

  • Module 16: Exhaustive Search (Regression and Neural Network).

  • Module 17: Uploading and Recalling Machine Learning to Adaptation.

  • Module 18: Security, Backup and Recovery and Consolidation.

  • Summing Up.


Upcoming Training Courses

US EST 4th December 2018: Jube Platform Training
1,497.00

The course runs from 9am to 5pm US Eastern Standard Time on 4th December 2018 for three days.

This is an online training course.

Comprehensive joining instructions, including Zoom, Virtual Whiteboard and Virtual Machine credentials will be sent in advance of the course commencement date.

Add To Cart
US EST 8th January 2019: Jube Platform Training
1,497.00

The course runs from 9am to 5pm US Eastern Standard Time on 8th January 2019 for three days.

This is an online training course.

Comprehensive joining instructions, including Zoom, Virtual Whiteboard and Virtual Machine credentials will be sent in advance of the course commencement date.

Add To Cart
US EST 5th February 2019: Jube Platform Training
1,497.00

The course runs from 9am to 5pm US Eastern Standard Time on 5th February 2019 for three days.

This is an online training course.

Comprehensive joining instructions, including Zoom, Virtual Whiteboard and Virtual Machine credentials will be sent in advance of the course commencement date.

Add To Cart

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