A university program that will provide you with flexibility thanks to its 100% online format. TECH adapts to the schedules of busy professionals!"

Combining the backend with Machine Learning is beneficial in a variety of contexts. In this way, programmers can automate highly repetitive tasks, such as extracting relevant information from large data sets. In the same vein, AI serves to boost application performance by predicting usage patterns, adjusting resource allocation and making real-time decisions to increase efficiency. This mechanism also uses recommendation algorithms to deliver personalized content to users who understand to users, including product or news suggestions based on their preferences.  

Aware of its importance,TECH has developed a Postgraduate diploma that will delve into the implementation of web projects using AI. Designed by a teaching staff specialized in this subject, the curriculum will provide advanced strategies for the creation of design patterns, databases and workspace spaces.   

Likewise, the syllabus will encourage professionals to detect possible failures during their processes, in order to create unit tests. At the same time, the didactic contents will be oriented to both optimization and performance management, using state-of-the-art Machine Learning tools. In addition, graduates will design large-scale systems that will be used to store the most relevant data. 

On the other hand, to strengthen the mastery of the syllabus, this university program applies the revolutionary Relearning teaching system, of which TECH is a pioneer. This promotes the assimilation of complex concepts through the natural and progressive reiteration of them. Similarly, the program is nourished by materials in various formats, such as infographics or explanatory videos. All this in a convenient 100% online modality, which allows each person to adjust their schedule to their responsibilities. All graduates need is an electronic device with Internet access. 

You will develop advanced strategies aimed at optimizing the deployment of your websites, responding quickly to market demands”

This Postgraduate diploma in Multiplatform Application Development using Artificial Intelligence contains the most complete and up-to-date educational program on the market. Its most notable features are:

• The development of case studies presented by experts in Multiplatform Application Development using AI 
• The graphic, schematic, and practical contents with which they are created, provide scientific and practical information on the disciplines that are essential for professional practice
• Practical exercises where the self-assessment process can be carried out to improve learning
• Its special emphasis on innovative methodologies
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

You will delve into machine translation between different programming languages, creating applications that work on a variety of platforms"

The program’s teaching staff includes professionals from the sector who contribute their work experience to this program, as well as renowned specialists from leading societies and prestigious universities.  

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations.  

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the academic year For this purpose, the students will be assisted by an innovative interactive video system created by renowned and experienced experts.

You will implement Clean Architecture in your procedures, to make your software projects more maintainable, scalable and adaptable to future changes"

Thanks to the Relearning system used by TECH you will reduce the long hours of study and memorization"

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!"