Una titulación universitaria que te aportará flexibilidad gracias a su formato 100% online. ¡TECH se adapta a las agendas de los profesionales ocupados

La combinación del backend con el Aprendizaje Automático resulta beneficioso en una variedad de contextos. Así pues, los programadores pueden automatizar tareas sumamente repetitivas, como la extracción de informaciones relevantes de grandes conjuntos de datos. En esta misma línea, la IA sirve para potenciar el rendimiento de las aplicaciones, al predecir patrones de uso, ajustar la asignación de recursos y tomas decisiones en tiempo real para elevar el nivel de eficiencia. Este mecanismo también usa algoritmos de recomendación para ofrecer contenido personalizado a los usuarios, que comprendan sugerencias de productos o noticias basadas en sus preferencias. 

Consciente de su importancia, TECH ha desarrollado una Postgraduate diploma que ahondará en la realización de proyectos web mediante IA. Diseñado por un cuadro docente especializado en esta materia, el plan de estudios proporcionará estrategias avanzadas para la creación de patrones de diseños, bases de datos y espacios workspace.  

Asimismo, el temario impulsará a los profesionales a detectar posibles fallos durante sus procesos, para crear test unitarios. Al mismo tiempo, los contenidos didácticos estarán orientados, tanto a la optimización como a la gestión del rendimiento, mediante las herramientas de Aprendizaje Automático más modernas. Además, los egresados diseñarán sistemas de gran escala que servirán para almacenar los datos más relevantes. 

Por otra parte, para afianzar el dominio del temario, esta titulación universitaria aplica el revolucionario sistema de enseñanza Relearning, del cual TECH es pionera. Este promueve la asimilación de conceptos complejos a través de la reiteración natural y progresiva de los mismos. De igual forma, el programa se nutre de materiales en diversos formatos, como las infografías o los vídeos explicativos. Todo ello en una cómoda modalidad 100% online, que permite ajustar los horarios de cada persona a sus responsabilidades. Lo único que necesitan los egresados es un dispositivo electrónico con acceso a Internet. 

Desarrollarás estrategia avanzadas destinadas a optimizar el despliegue de tus webs, respondiendo con rapidez a las demandas del mercado” 

Esta Postgraduate diploma en Multiplatform Application Development using Artificial Intelligence  contiene el programa educativo más completo y actualizado del mercado. Sus características más destacadas son: 

• El desarrollo de casos prácticos presentados por expertos en Desarrollo de Aplicaciones Multiplataforma mediante IA 
• Los contenidos gráficos, esquemáticos y eminentemente prácticos con los que está concebido recogen una información científica y práctica sobre aquellas disciplinas indispensables para el ejercicio profesional 
• Los ejercicios prácticos donde realizar el proceso de autoevaluación para mejorar el aprendizaje 
• Su especial hincapié en metodologías innovadoras  
• Las lecciones teóricas, preguntas al experto, foros de discusión de temas controvertidos y trabajos de reflexión individual 
• La disponibilidad de acceso a los contenidos desde cualquier dispositivo fijo o portátil con conexión a internet 

Profundizarás en la traducción automática entre diferentes lenguajes de programación, creando aplicaciones que funcionen en una variedad de plataformas” 

El programa incluye en su cuadro docente a profesionales del sector que vierten en esta capacitación la experiencia de su trabajo, además de reconocidos especialistas de sociedades de referencia y universidades de prestigio.  

Su contenido multimedia, elaborado con la última tecnología educativa, permitirá al profesional un aprendizaje situado y contextual, es decir, un entorno simulado que proporcionará una capacitación inmersiva programada para entrenarse ante situaciones reales.  

El diseño de este programa se centra en el Aprendizaje Basado en Problemas, mediante el cual el profesional deberá tratar de resolver las distintas situaciones de práctica profesional que se le planteen a lo largo del curso académico. Para ello, contará con la ayuda de un novedoso sistema de vídeo interactivo realizado por reconocidos expertos. 

Implementarás en tus procedimientos la Clean Architecture, para que tus proyectos de software sean más mantenibles, escalables y adaptables a cambios futuros” 

Gracias al sistema Relearning que emplea TECH, reducirás las largas horas de estudio y memorización” 

Thanks to this course, the programmer will master both the configuration of the development environment related to AI software and repository management. The integration of Machine Learning elements in Visual Studio Code, as well as code optimization using ChatGPT, will also be highlighted. In addition, the professional will delve into the aspects of software architecture, including performance, stability and maintainability. You will also delve into the practices of highly proficient software developers and emphasis will be placed on the optimization of the deployment process as well as cloud computing. 

You will get a comprehensive view on the application of Artificial Intelligence in software development.  And in as little as in 6 months!" 

Module 1. Improving Software Development Productivity with AI

1.1. Prepare a Suitable Development Environment

1.1.1. Selection of Essential Tools for AI Development
1.1.2. Configuration of the Chosen Tools
1.1.3. Implementation of CI/CD Ppipelines Adapted to AI Projects
1.1.4. Efficient Management of Dependencies and Versions in Development Environments

1.2. Essential AI Extensions for Visual Studio Code

1.2.1. Exploring and Selecting AI Extensions for Visual Studio Code
1.2.2. Integration of Static and Dynamic Analysis Tools in the SDI
1.2.3. Automation of Repetitive Tasks with Specific Extensions
1.2.4. Customization of the Development Environment to Improve Efficiency

1.3. No-code  Design of User Interfaces with AI Elements

1.3.1. No-code  Design Principles and Their Application to User Interfaces
1.3.2. Incorporation of AI Elements in the Visual Design of Interfaces
1.3.3. Tools and Platforms for No-code Creation of Intelligent Interfaces
1.3.4. Evaluation and Continuous Improvement of No-code Interfaces with AI

1.4. Code Optimization using ChatGPT

1.4.1. Identifying Duplicate Code
1.4.2. Refactor
1.4.3. Create Readable Code
1.4.4. Understanding What Code Does
1.4.5. Improving Variable and Function Names
1.4.6. Automatic Documentation Creation

1.5. Repository Management with AI

1.5.1. Automation of Version Control Processes with AI Techniques
1.5.2. Conflict Detection and Automatic Resolution in Collaborative Environments
1.5.3. Predictive Analysis of Changes and Trends in Code Repositories
1.5.4. Improved Organization and Categorization of Repositories using AI

1.6. Integration of AI in Database Management

1.6.1. Query and Performance Optimization Using AI Techniques
1.6.2. Predictive Analysis of Database Access Patterns
1.6.3. Implementation of Recommender Systems to Optimize Database Structure
1.6.4. Monitoring and Proactive Detection of Potential Problems in Databases

1.7. Fault Finding and Creation of Unit Tests with AI

1.7.1. Automatic Generation of Test Cases Using AI Techniques
1.7.2. Early Detection of Vulnerabilities and Bugs using Static Analysis with AI
1.7.3. Improving Test Coverage by Identifying Critical Areas with AI

1.8. Pair Programming with GitHub Copilot

1.8.1. Integration and Effective Use of GitHub Copilot in  Pair ProgrammingSessions
1.8.2. Integration Improvements in Communication and Collaboration between Developers with GitHub Copilot
1.8.3. Integration Strategies for Making the Most of Code Hints Generated by GitHub Copilot
1.8.4. Integration Case Studies and Best Practices in AI-assisted Pair Programming

1.9. Automatic Translation between Programming Languages

1.9.1. Programming Language Specific Machine Translation Tools and Services
1.9.2. Adapting Machine Translation Algorithms to Development Contexts
1.9.3. Improving Interoperability between Different Languages by Machine Translation
1.9.4. Assessing and Mitigating Potential Challenges and Limitations of Machine Translation

1.10. Recommended AI Tools to Improve Productivity

1.10.1. Comparative Analysis of AI Tools for Software Development
1.10.2. Integration of AI Tools in Workflows
1.10.3. Automation of Routine Tasks with AI Tools
1.10.4. Evaluating and Selecting Tools Based on Context and Project Requirements

Module 2. Web Projects with AI

2.1. Preparation of the Working Environment for Web Development with AI

2.1.1. Configuration of Web Development Environments for Projects with Artificial Intelligence
2.1.2. Selection and Preparation of Essential Tools for AI  Web Development
2.1.3. Integration of Specific Libraries and Frameworks for Web Projects with Artificial Intelligence
2.1.4. Implementation of Best Practices in the Configuration of Collaborative Development Environments

2.2. Workspace Creation for AI Projects

2.2.1. Effective Design and Organization of Workspaces for Web Projects with Artificial Intelligence Components
2.2.2. Use of Project Management and Version Control Tools in the Workspace
2.2.3. Strategies for Efficient Collaboration and Communication in the Development Team
2.2.4. Adaptation of the Workspace to the Specific Needs of AI Web Projects

2.3. Design Patterns in AI Products

2.3.1. Identification and Application of Common Design Patterns in User Interface with Artificial Intelligence Components
2.3.2. Development of Specific Patterns to Improve User Experience in Web Projects with AI
2.3.3. Integration of Design Patterns in the Overall Architecture of AI Web Projects
2.3.4. Evaluation and Selection of Adequate Design Patterns according to the Project Context

2.4. Frontend Development with AI

2.4.1. Integration of AI Models into the Presentation Layer of Web Projects
2.4.2. Development of Adaptive User Interfaces with Artificial Intelligence Elements
2.4.3. Implementation of Natural Language Processing (NLP) Functionalities in the Frontend
2.4.4. Strategies for Performance Optimization in Frontend Development with AI

2.5. Database Creation

2.5.1. Selection of Database Technologies for Web Projects with Artificial Intelligence
2.5.2. Design of Database Schemas for Storing and Managing AI-Related Data
2.5.3. Implementation of Efficient Storage Systems for Large Volumes of Data Generated by AI Models
2.5.4. Strategies for the Security and Protection of Sensitive Data in AI Web Project Databases

2.6. Back-End Development with AI

2.6.1. Integration of AI Services and Models in the Backend Business Logic
2.6.2. Development of Specific APIs and Endpoints for Communication between the Frontend and AI Components
2.6.3. Implementation of Data Processing and Decision Making Logic in the Backend with Artificial Intelligence
2.6.4. Strategies for Scalability and Performance in the Backend Development of Web Projects with AI

2.7. Optimizing Your Web Deployment Process

2.7.1. Automating Web Project Build and Deployment Processes with AI
2.7.2. Implementing CI/CD Pipelines Tailored to Web Applications with Artificial Intelligence Components
2.7.3. Strategies for Efficient Release and Upgrade Management in Continuous Deployments
2.7.4. Post-Deployment Monitoring and Analysis for Continuous Process Improvement

2.8. AI in Cloud Computing

2.8.1. Integration of Artificial Intelligence Services in Cloud Computing Platforms
2.8.2. Development of Scalable and Distributed Solutions using Cloud Services with AI Capabilities
2.8.3. Strategies for Efficient Resource and Cost Management in Cloud Environments with AI-enabled Web Applications
2.8.4. Evaluation and Comparison of Cloud Service Providers for AI-enabled Web Projects

2.9. Creating an AI-enabled Project for LAMP Environments

2.9.1. Adaptation of Web Projects based on the LAMP Stack to include Artificial Intelligence Components
2.9.2. Integration of AI-specific Libraries and Frameworks in LAMP Environments
2.9.3. Development of AI Functionalities Complementing the Traditional LAMP Architecture
2.9.4. Strategies for Optimization and Maintenance in Web Projects with AI in LAMP Environments

2.10. Creating an AI-enabled Project for MEVN Environments

2.10.1. Integration of MEVN Stack Technologies and Tools with AI Components
2.10.2. Development of Modern and Scalable Web Applications in MEVN Environments with AI Capabilities
2.10.3. Implementation of Data Processing and Machine Learning functionalities in MEVN projects
2.10.4. Strategies for Imrpoving Performance and Security Enhancement of AI-enabled Web Applications in MEVN Environments

Module 3. AI-enabled Mobile Applications

3.1. Preparation of Working Environment for Mobile Development with AI

3.1.1. Configuration of Mobile Development Environments for Projects with Artificial Intelligence
3.1.2. Selection and Preparation of Specific Tools for  Mobile Application Development with AI
3.1.3. Integration of AI Libraries and Frameworks in Mobile Development Environments
3.1.4. Configuration of Emulators and Real Devices for Testing Mobile Applications with AI Components

3.2. Creating a Workspace with GitHub Copilot

3.2.1. Integration of GitHub Copilot in Mobile Development Environments
3.2.2. Effective Use of GitHub Copilot for Code Generation in AI Projects
3.2.3. Strategies for Developer Collaboration when using GitHub Copilot in the Workspace
3.2.4. Best Practices and Limitations in the Use of GitHub Copilot in Mobile Application Development with AI

3.3. Firebase Configuration

3.3.1. Initial Configuration of a Firebase Project for Mobile Development
3.3.2. Firebase Integration in Mobile Applications with Artificial Intelligence Functionalities
3.3.3. Use of Firebase Services as a Database, Authentication and Notifications in AI Projects
3.3.4. Strategies for Real-Time Data and Event Management in Firebase-enabled Mobile Applications

3.4. Concepts of Clean Architecture, DataSources, Repositories

3.4.1. Fundamental Principles of Clean Architecture in Mobile Development with AI
3.4.2. Implementation of DataSources and Repositories Layers in Clean Architectures
3.4.3. Design and Structuring of Components in Mobile Projects with a Focus on Clean Architecture
3.4.4. Benefits and Challenges of Implementing Clean Architecture in Mobile Applications with AI

3.5. Authentication Screen Creation

3.5.1. Design and Development of User Interfaces for Authentication Screens in Mobile Applications with AI
3.5.2. Integration of Authentication Services with Firebase in the Login Screen
3.5.3. Use of Security and Data Protection Techniques in the Authentication Screen
3.5.4. Personalization and Customization of the User Experience on the Authentication Screen

3.6. Dashboardand Navigation Creation

3.6.1. Dashboard Design and Development with Artificial Intelligence Elements
3.6.2. Implementation of Efficient Navigation Systems in Mobile Applications with AI
3.6.3. Integration of AI Functionalities in the Dashboard to Improve User Experience

3.7. Creation of Listing Screen

3.7.1. Development of User Interfaces for AI-enabled Mobile Application Listing Displays
3.7.2. Integration of Recommendation and Filtering Algorithms in the Listing Screen
3.7.3. Use of Design Patterns for Effective Data Presentation in the Listing Screen
3.7.4. Strategies for Efficient Real-Time Data Loading in the Listing Screen

3.8. Creating Detail Screen

3.8.1. Design and Development of Detailed User Interfaces for the Presentation of Specific Information
3.8.2. Integration of AI Functionalities to Enrich the Detail Screen
3.8.3. Implementation of Interactions and Animations in the Detail Screen
3.8.4. Strategies for Performance Optimization in Loading and Detail Display in AI-enabled Mobile Applications

3.9. Creating Settings Screen

3.9.1. Development of User Interfaces for Configuration and Settings in AI-enabled Mobile Applications
3.9.2. Integration of Custom Settings Related to AI Components
3.9.3. Implementing Customization Options and Preferences in the Configuration Screen
3.9.4. Strategies for Usability and Clarity in the Presentation of Options in the Settings Screen

3.10. Creating Icons, Splash  and Graphic Resources for Your App with AI

3.10.1. Designing and Creating Attractive Icons to Represent Your AI Mobile Application
3.10.2. DevelopingSplash Screens with Impressive Visual Elements
3.10.3. Selection and Adaptation of Graphic Resources to Enhance the Aesthetics of the Mobile Application
3.10.4. Strategies for Consistency and Visual Branding in AI Application Graphics Elements

You will get a comprehensive view on the application of Artificial Intelligence in software development.  And in as little as in 6 months!"