Online or onsite, instructor-led live Python skope-rules training courses demonstrate through interactive hands-on practice how to use skope-rules to automatically generate rules based on existing data sets.
Python skope-rules training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Bonn onsite live Python skope-rules trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
Our training facilities are located at Mozartstraße 4-10 in Bonn. Our spacious traini...
Overview
Our training facilities are located at Mozartstraße 4-10 in Bonn. Our spacious training rooms are located southwest of the city centre and offer optimal training conditions for your needs.
Arrival
The NobleProg training facilities are conveniently located near the Bonn main station. In the west you reach the motorway A565.
Parking
You will find numerous parking spaces around our training rooms.
Local Infrastructure
In downtown Bonn you will find numerous hotels and restaurants..
This course introduces the student to the Python language. Upon completion of this class, the student will be able to write non trivial Python programs dealing with a wide variety of subject matter domains. Topics include language components, working with a professional IDE, control flow constructs, strings, I/O, collections, classes, modules, and regular expressions. The course is supplemented with many hands-on labs, solutions, and code examples.
After Completing the course students will be able to demonstrate knowledge and understanding of Python Security Principles.
In this instructor-led, live training in Bonn (onsite or remote), participants will learn how to use Python skope-rules to automatically generate rules based on existing data sets.
By the end of this training, participants will be able to:
Use skope-rules to extract rules from available data.
Apply skope-rules to carry out classification, particularly useful in supervised anomaly detection, or imbalanced classification.
Generate rules for classifying new incoming data.
Fit rules to address real-world problems in fraud detection, predictive maintenance, intrusion detection, insurance application approvals, etc.
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