Cursos de Inteligencia Artificial y Data Science

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. 

Voice technologies are rapidly reshaping how businesses interact with customers, automate operations, and analyze conversations. Today’s AI systems can deliver fast, accurate, and natural-sounding speech capabilities across multiple languages, supporting use cases such as customer self-service, live agent assistance, real-time analytics, and more.In this course, learners will explore how to apply IBM Watson Speech to Text and Watson Text to Speech to build voice-enabled solutions tailored to their unique business needs.By the end of the course, participants will have the skills to design, customize, and integrate AI-powered voice solutions that deliver seamless, human-like interactions across multiple platforms.

Learn to effectively use the core capabilities of watsonx Assistant to plan, build, troubleshoot, and maintain a virtual assistant. This course provides an in-depth exploration of several key areas of watsonx Assistant, equipping students with the technical knowledge required to create advanced virtual assistants. 

In this course, students will learn to use scientific methods, processes, and algorithms to identify patterns, trends, and insights from data – with the goal of enabling better decision-making.  Students will use watsonx.ai to synthesize skills from statistics, computer science, and management information systems to analyze and interpret complex datasets. This course includes a practical component with hands-on exercises focused on collecting, preparing, mining, and visualizing data and then building a machine learning and foundation model with methods on how to deploy them. 

Voice technologies are rapidly reshaping how businesses interact with customers, automate operations, and analyze conversations. Today’s AI systems can deliver fast, accurate, and natural-sounding speech capabilities across multiple languages, supporting use cases such as customer self-service, live agent assistance, real-time analytics, and more.In this course, learners will explore how to apply IBM Watson Speech to Text and Watson Text to Speech to build voice-enabled solutions tailored to their unique business needs.By the end of the course, participants will have the skills to design, customize, and integrate AI-powered voice solutions that deliver seamless, human-like interactions across multiple platforms.

In this course, the learner is guided through a realistic scenario of administering and customizing the watsonx governance console for an organization wishing to govern the lifecycles of AI initiatives. The course includes user management, profile assignment and customization, modeling real-world organization structures in the governance console, identification of model risks and associated controls, customized risk assessment using questionnaires, and building specific workflows to manage AI use cases.

This hands-on course introduces learners to the principles and practices of Large Language Model Operations (LLMOps), focusing on the development, deployment, and governance of AI models using IBM's watsonx.ai platform. Participants will explore the IBM LLM workflow, utilize Prompt Lab for model prompting, and develop Retrieval-Augmented Generation (RAG) models with AutoAI. The course emphasizes practical experience through UI-based labs, enabling learners to build, deploy, and monitor AI models effectively.

Este curso proporciona una visión clara, realista y práctica de la Inteligencia Artificial actual, orientada a su aplicación directa en el trabajo diario. No se centra en programación ni aspectos técnicos, sino en cómo pensar, comunicarse y trabajar con IA para aumentar la productividad, mejorar la calidad de las decisiones y automatizar tareas habituales.
El objetivo es que los participantes entiendan cómo funciona la IA a nivel conceptual, aprendan a interactuar eficazmente con ella y sepan integrarla como copiloto profesional, no como sustituto.

Learn to effectively use the core capabilities of watsonx Assistant to plan, build, troubleshoot, and maintain a virtual assistant. This course provides an in-depth exploration of several key areas of watsonx Assistant, equipping students with the technical knowledge required to create advanced virtual assistants. 

Este curso intensivo de 30 horas ofrece una formación completa en Microsoft Copilot Studio, la herramienta avanzada de Microsoft para la creación de agentes conversacionales y automatización de tareas mediante inteligencia artificial. A lo largo del curso, los participantes aprenderán desde los fundamentos hasta la personalización avanzada, integrando Copilot Studio con servicios empresariales clave como Azure OpenAI, Dataverse, Power Automate y Power BI.

El curso combina teoría y práctica con ejercicios aplicados a escenarios reales, incluyendo soporte técnico, automatización de flujos de trabajo, integración con APIs y desarrollo de dashboards interactivos. Al finalizar, los estudiantes serán capaces de diseñar, implementar y gestionar agentes de IA en entornos empresariales con seguridad y eficiencia

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.

This course provides an in-depth exploration of the Watson Discovery API to build an enterprise search tool. You learn how to create regular expressions, upload dictionaries, customize queries, conduct relevancy training, and extract results. 

Watson Discovery: Intelligent Document Processing  will dive deeply into Intelligent Document Processing. You will learn how to create regular expressions, import rule-based models, customize query results, conduct web crawling, and teach the domain language to Watson Discovery to enhance the relevance and accuracy of your results.

Watson Discovery: Intelligent Document Processing will dive deeply into Intelligent Document Processing. You will learn how to create regular expressions, import rule-based models, customize query results, conduct web crawling, and teach the domain language to Watson Discovery to enhance the relevance and accuracy of your results.

Building AI Assistants with IBM watsonx Assistant aims to equip non-technical business users with the skills and experience needed to plan, design, build, test, and deploy their own AI Assistant. The student will learn how to create assistant services and instances, create assistants from scratch, handle state, upload existing assistants, copy actions between one assistant and another, set conversational paths between actions, test, deploy and analyze user interactions over time.  

The IBM Cloud Pak for AIOps platform delivers visibility into performance data and dependencies across environments. It aims to enhance the efficiency of IT operations, reduce costs, and ensure that systems are robust and secure. This course provides instructions on how to do an air-gapped installation of IBM Cloud Pak for AIOps v4.10.

This course offers data engineers more experience with watsonx.data 2.0.2. You will gain knowledge in the areas such as infrastructure management, data management, running SQL statements by using watsonx.data console (user interface). You gain skills in access control, ingesting data, creating schemas, running queries that combines data from multiple data sources, and rolling back a table to previous point in time.

This course offers data engineers more experience with watsonx.data on Red Hat OpenShift Container Platform. You gain knowledge in areas such as user and group management, access controls within watsonx.data, integrating IBM Knowledge Catalog with watsonx.data, and what the integration with IBM Knowledge Catalog provides. You gain skills in access control, IBM Knowledge Catalog policies, and how to manage watsonx.data after integration with IBM Knowledge Catalog is in place. The course is designed to build upon the watsonx.data intermediate course and goes into more depth in the data governance area of watsonx.data. Integration between watsonx.data and IBM Knowledge Catalog allows you to manage data within the Data Lakehouse and other data sources with a single data governance product. This integration ensures that all data is governed within a centralized data governance product.