Cursos de Python

IBM Safer Payments is an innovative real-time payment fraud prevention and detection solution for all cashless payment types. IBM Safer Payments provides not only model capabilities based on inbuilt tools, but also the option to import externally built fraud models for real-time decisioning.  In this course, all of the IBM Safer Payments model capabilities are presented in details. The following modelling concepts are covered: index, profiling techniques (with and without index sequence), model components comprised of rulesets, PMML, Python and Internal Random Forest, elements of the simulation environment including Rule Generation and Internal Random Forest, as well as the sampling techniques. All these concepts will be followed by the hands-on exercises that students are expected to execute.

IBM Safer Payments is an innovative real-time payment fraud prevention and detection solution for all cashless payment types. IBM Safer Payments provides not only model capabilities based on inbuilt tools, but also the option to import externally built fraud models for real-time decisioning.  In this course, all of the IBM Safer Payments model capabilities are presented in details. The following modelling concepts are covered: index, profiling techniques (with aIBM Safer Payments is an enterprise fraud detection solution. It is a designed and focused on real time payment transaction processing. Safer Payments provides complex and dynamic rules building, evaluation and execution. Built with real time performance, management, and redundancy in mind.nd without index sequence), model components comprised of rulesets, PMML, Python and Internal Random Forest, elements of the simulation environment including Rule Generation and Internal Random Forest, as well as the sampling techniques. All these concepts will be followed by the hands-on exercises that students are expected to execute.

In this course, all of the IBM Safer Payments model capabilities are presented in detail. The following modelling concepts are covered: index, profiling techniques (with and without index sequence), model components comprised of rulesets, PMML, Python and Internal Random Forest, elements of the simulation environment including Rule Generation and Internal Random Forest, as well as the sampling techniques. All these concepts will be followed by the hands-on exercises.

The goal of this course is to provide the student with a tangible understanding and a real hands-on experience with generative AI applications deployed and optimized for IBM Power systems. This self-paced virtual course uses video lectures, review questions, and virtual lab machine exercises to provide the student with foundational knowledge and experience about the topics covered in the course. In the lecture, the student begins by learning the basics of generative AI, and the basic components of a generative AI application, then applies these concepts to real-world examples on Power where the student learns Power offerings for AI workloads, package management fundamentals in Python, Operating System deployment strategies on Power, and Power hardware use cases. The lab exercises will start with viewing and manipulating files in a basic AI application running on IBM Power Red Hat Enterprise Linux. They will have hands-on experience identifying the components of that AI application and their connection to other components, and will add functionalities to the application that demonstrate more advanced AI application techniques like frameworks, prompt tuning, and conversation memory. Then, the students will go through the process of setting up a Python virtual environment, investigating Power hardware resources, and using the Hardware Management Console (HMC) to ensure memory and other resources are optimized for AI workloads. 

El curso Programación Orientada a Objetos tiene como objetivo introducir al alumno en la Programación Orientada a Objetos con Python, partiendo de los fundamentos del lenguaje (sintaxis, estructuras de control, funciones y excepciones) hasta la aplicación de los principios clave de la POO. Se abordan la creación de clases, uso de métodos y atributos, así como conceptos avanzados como herencia, polimorfismo y abstracción. Además, se exploran herramientas propias de Python, patrones de diseño y modelado UML para desarrollar aplicaciones organizadas, reutilizables y mantenibles.

Este curso introduce de forma práctica en el uso de Python y su ecosistema científico para resolver problemas de Machine Learning , desde el análisis de datos hasta la construcción y validación de modelos supervisados y no supervisados.

El curso "Introducción Integral a la IA con Python: Programación, Modelado y Despliegue" ofrece una amplia introducción a los conceptos fundamentales de la inteligencia artificial (IA) utilizando el lenguaje de programación Python.

Este curso está diseñado para proporcionar a los participantes una base sólida en el lenguaje de programación Python.
El temario del curso abarca una amplia gama de temas, desde la introducción al entorno de desarrollo y los conceptos básicos de Python hasta temas más avanzados, como la programación orientada a objetos y el manejo de excepciones.

In this 1-day instructor led course the students will learn about automation scripting in Maximo, including how automation scripts can be used to extend functionality using code written in Python or JavaScript. This includes learning about launch points, variables and Maximo business objects in Maximo and how to use them and write scripts and run them with different launch points. Along with a combination of procedural information and demonstrations, this course also has hands-on lab exercises using a Red Hat OpenShift Maximo Manage environment for you to try out.

In this course, you will learn about automation scripting in Maximo, including how automation scripts can be used to extend functionality using code written in Python or JavaScript. This includes learning about launch points, variables and Maximo business objects in Maximo and how to use them and write scripts and run them with different launch points.Along with a combination of procedural information and demonstrations, this course also has hands-on lab exercises using a Red Hat OpenShift Maximo Manage environment for you to try out.

IBM Safer Payments is an enterprise fraud detection solution. It is a designed and focused on real time payment transaction processing. Safer Payments provides complex and dynamic rules building, evaluation and execution. Built with real time performance, management, and redundancy in mind.In this course, all of the IBM Safer Payments model capabilities are presented in detail. The following modelling concepts are covered: index, profiling techniques (with and without index sequence), model components comprised of rulesets, PMML, Python and Internal Random Forest, elements of the simulation environment including Rule Generation and Internal Random Forest, as well as the sampling techniques. All these concepts will be followed by the hands-on exercises.

IBM Safer Payments is an innovative real-time payment fraud prevention and detection solution for all cashless payment types. IBM Safer Payments provides not only model capabilities based on inbuilt tools, but also the option to import externally built fraud models for real-time decisioning. In this course, all of the IBM Safer Payments model capabilities are presented in details. The following modelling concepts are covered: index, profiling techniques (with and without index sequence), model components comprised of rulesets, PMML, Python and Internal Random Forest, elements of the simulation environment including Rule Generation and Internal Random Forest, as well as the sampling techniques. All these concepts will be followed by the hands-on exercises that students are expected to complete.

This course is designed to teach the installation and administration of open source packages on IBM i as well as teach how to use some of the open source packages that can be downloaded. Class activities include showing how to download and manage open source packages using Access Client Solutions, and how to use python, node.js, and PHP to access data in different ways on the IBM i.