With this 100% online Professional master’s degree, you will understand the most advanced technologies in AI, mastering cutting-edge tools and techniques to improve efficiency and accuracy in translation and interpreting” 

Artificial Intelligence (AI) is rapidly transforming the field of translation and interpretation, with significant advances in the accuracy and efficiency of these processes. Tools such as Google Translate and DeepL use advanced neural networks to provide real-time translations and capture complex linguistic nuances. At the same time, emerging technologies are facilitating instantaneous communication between speakers of different languages through different languages through real-time interpreting applications. 

This is how this Professional master’s degree was created, which will delve into the fundamentals of linguistic models, exploring from traditional approaches to the most advanced ones in AI. In this sense, speech recognition and sentiment analysis will be addressed, equipping professionals with the necessary tools to implement these technologies in practical contexts and face the emerging challenges in the field. 

In addition, Neural Machine Translation (NMT) and Natural Language Processing (NLP) will be explored, using specialized tools and platforms that allow instantaneous translation. It will also include a critical evaluation of the quality of real-time translations and a reflection on the ethical and social aspects associated with their implementation.  

Finally, the development and optimization of speech recognition platforms will be addressed, as well as how to create chatbots using AI, applying natural language processing techniques to improve multilingual interaction and user experience. In addition, it will delve into the ethical and social challenges that emerge in these areas, ensuring that experts handle themselves effectively and ethically. 

In this way, TECH has established a comprehensive, fully online university program, allowing graduates to access educational materials through an electronic device with an Internet connection. This eliminates the need to travel to a physical center and adhere to a fixed schedule. Additionally, it incorporates the revolutionary Relearning methodology, which is based on the repetition of key concepts to achieve a better understanding of the contents. 

You will implement innovative solutions, such as real-time machine translation and speech recognition systems, a competitive advantage in a constantly evolving job market” 

This Professional master’s degree in Artificial Intelligence in Translation and Interpreting contains the most complete and up-to-date program on the market. The most important features include: 

• The development of case studies presented by experts in Artificial Intelligence focused on Translation and Interpreting 
• The graphic, schematic, and practical contents with which they are created, provide 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 immerse yourself in a comprehensive exploration of linguistic models, ranging from traditional to modern approaches, thanks to an extensive library of innovative multimedia resources” 

The program’s teaching staff includes professionals from the field who contribute their work experience to this educational 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 course. For this purpose, students will be assisted by an innovative interactive video system created by renowned and experienced experts. 

You will cover the principles of Neural Machine Translation (NMT) and Natural Language Processing (NLP), including the use of specialized tools and platforms. What are you waiting for to enroll?"

You will examine the integration of machine translation models and linguistic resources, as well as the user experience at the interface of these tools. With all TECH's quality guarantees!"

This program is designed to provide professionals with an in-depth understanding of linguistic models and their integration with AI technologies, as well as practical training in real-time translation tools, AI-assisted translation platforms and speech recognition technologies for machine interpreting. In addition, it will focus on interface design and multilingual chatbots, providing a comprehensive overview of how AI is revolutionizing the industry. It will also address the associated ethical and social challenges, ensuring that graduates acquire advanced technical skills. 

The main objective of this Professional master’s degree will be to offer comprehensive training, combining classical linguistic theory with the most advanced applications of AI in the field of translation and interpreting” 

General Objectives

• Understand the theoretical foundations of Artificial Intelligence 
• Study the different types of data and understand the data lifecycle 
• Evaluate the crucial role of data in the development and implementation of AI solutions 
• Delve into algorithms and complexity to solve specific problems 
• Explore the theoretical basis of neural networks for Deep Learning development 
• Explore bio-inspired computing and its relevance in the development of intelligent systems 
• Understand classical and modern linguistic models and their application in Artificial Intelligence  
• Acquire skills to use and optimize AI tools in real-time translation, ensuring accuracy and fluency in multilingual contexts 
• Become skilled in the use of the main AI-assisted translation platforms and tools, integrating them effectively into the professional workflow 
• Learn how to integrate speech recognition technologies into machine interpreting systems, improving accessibility and efficiency 
• Design and program multilingual chatbots using AI, enhancing interaction with users in different languages 
• Develop criteria and methods for assessing the quality of translations and interpretations performed with AI tools
• Integrate AI tools and platforms into the workflow of translators and interpreters, optimizing productivity and consistency 
• Train in identifying and resolving ethical and social challenges related to the use of Artificial Intelligence in translation and interpreting 
• Explore and implement innovations in the field of AI-assisted translation and interpretation, anticipating emerging trends 
• Equip yourself with the necessary skills to lead projects and teams in the implementation of AI solutions in the field of translation and interpreting 

Specific Objectives

Module 1. Fundamentals of Artificial Intelligence

• Analyze the historical evolution of Artificial Intelligence, from its beginnings to its current state, identifying key milestones and developments
• Understand the functioning of neural networks and their application in learning models in Artificial Intelligence
• Study the principles and applications of genetic algorithms, analyzing their usefulness in solving complex problems
• Analyze the importance of thesauri, vocabularies and taxonomies in the structuring and processing of data for AI systems

Module 2. Data Types and Data Life Cycle

• Understand the fundamental concepts of statistics and their application in data analysis
• Identify and classify the different types of statistical data, from quantitative to qualitative data
• Analyze the life cycle of data, from generation to disposal, identifying key stages
• Explore the initial stages of the data life cycle, highlighting the importance of data planning and structure
• Study data collection processes, including methodology, tools and collection channels
• Explore the Datawarehouse concept, with emphasis on the elements that comprise it and its design

Module 3. Data in Artificial Intelligence

• Master the fundamentals of data science, covering tools, types and sources for information analysis
• Explore the process of transforming data into information using data mining and visualization techniques
• Study the structure and characteristics of datasets, understanding their importance in the preparation and use of data for Artificial Intelligence models
• Use specific tools and best practices in data handling and processing, ensuring efficiency and quality in the implementation of Artificial Intelligence

Module 4. Data Mining: Selection, Preprocessing and Transformation

• Master the techniques of statistical inference to understand and apply statistical methods in data mining
• Perform detailed exploratory analysis of data sets to identify relevant patterns, anomalies, and trends
• Develop skills for data preparation, including data cleaning, integration, and formatting for use in data mining
• Implement effective strategies for handling missing values in datasets, applying imputation or elimination methods according to context
• Identify and mitigate noise present in data, using filtering and smoothing techniques to improve the quality of the data set
• Address data preprocessing in Big Data environments

Module 5. Algorithm and Complexity in Artificial Intelligence

• Introduce algorithm design strategies, providing a solid understanding of fundamental approaches to problem solving
• Analyze the efficiency and complexity of algorithms, applying analysis techniques to evaluate performance in terms of time and space
• Study and apply sorting algorithms, understanding their performance and comparing their efficiency in different contexts
• Explore tree-based algorithms, understanding their structure and applications
• Investigate algorithms with Heaps, analyzing their implementation and usefulness in efficient data manipulation
• Analyze graph-based algorithms, exploring their application in the representation and solution of problems involving complex relationships
• Study Greedy algorithms, understanding their logic and applications in solving optimization problems.
• Investigate and apply the backtracking technique for systematic problem solving, analyzing its effectiveness in various scenarios

Module 6. Intelligent Systems

• Explore agent theory, understanding the fundamental concepts of its operation and its application in Artificial Intelligence and software engineering
• Study the representation of knowledge, including the analysis of ontologies and their application in the organization of structured information
• Analyze the concept of the semantic web and its impact on the organization and retrieval of information in digital environments
• Evaluate and compare different knowledge representations, integrating these to improve the efficiency and accuracy of intelligent systems

Module 7: Machine Learning and Data Mining

• Introduce the processes of knowledge discovery and the fundamental concepts of machine learning
• Study decision trees as supervised learning models, understanding their structure and applications
• Evaluate classifiers using specific techniques to measure their performance and accuracy in data classification
• Study neural networks, understanding their operation and architecture to solve complex machine learning problems
• Explore Bayesian methods and their application in machine learning, including Bayesian networks and Bayesian classifiers
• Analyze regression and continuous response models for predicting numerical values from data
• Study clustering techniques to identify patterns and structures in unlabeled data sets
• Explore text mining and natural language processing (NLP), understanding how machine learning techniques are applied to analyze and understand text

Module 8. Neural Networks, the Basis of Deep Learning

• Master the fundamentals of Deep Learning, understanding its essential role in Deep Learning
• Explore the fundamental operations in neural networks and understand their application in model building
• Analyze the different layers used in neural networks and learn how to select them appropriately
• Understand the effective linking of layers and operations to design complex and efficient neural network architectures
• Use trainers and optimizers to tune and improve the performance of neural networks
• Explore the connection between biological and artificial neurons for a deeper understanding of model design

Module 9. Deep Neural Networks Training

• Solve gradient-related problems in deep neural network training
• Explore and apply different optimizers to improve the efficiency and convergence of models
• Program the learning rate to dynamically adjust the convergence speed of the model
• Understand and address overfitting through specific strategies during training
• Apply practical guidelines to ensure efficient and effective training of deep neural networks
• Implement Transfer Learning as an advanced technique to improve model performance on specific tasks
• Explore and apply Data Augmentation techniques to enrich datasets and improve model generalization
• Develop practical applications using Transfer Learning to solve real-world problems

Module 10. Model Customization and Training with TensorFlow

• Master the fundamentals of TensorFlow and its integration with NumPy for efficient data management and calculations.
• Customize models and training algorithms using the advanced capabilities of TensorFlow
• Explore the tfdata API to efficiently manage and manipulate datasets
• Implement the TFRecord format for storing and accessing large datasets in TensorFlow
• Use Keras preprocessing layers to facilitate the construction of custom models
• Explore the TensorFlow Datasets project to access predefined datasets and improve development efficiency
• Develop a Deep Learning application with TensorFlow, integrating the knowledge acquired in the module
• Apply in a practical way all the concepts learned in building and training custom models with TensorFlow in real-world situations

Module 11. Deep Computer Vision with Convolutional Neural Networks

• Understand the architecture of the visual cortex and its relevance in Deep Computer Vision
• Explore and apply convolutional layers to extract key features from images
• Implement clustering layers and their use in  Deep Computer Vision models with Keras
• Analyze various Convolutional Neural Network (CNN) architectures and their applicability in different contexts
• Develop and implement a CNN ResNet using the Keras library to improve model efficiency and performance
• Use pre-trained Keras models to leverage transfer learning for specific tasks
• Apply classification and localization techniques in Deep Computer Vision environments
• Explore object detection and object tracking strategies using Convolutional Neural Networks

Module 12. Natural Language Processing (NLP) with Recurrent Neural Networks (RNN) and Attention

• Developing skills in text generation using Recurrent Neural Networks (RNN)
• Apply RNNs in opinion classification for sentiment analysis in texts
• Understand and apply attentional mechanisms in natural language processing models
• Analyze and use Transformers models in specific NLP tasks
• Explore the application of Transformers models in the context of image processing and computer vision
• Become familiar with the Hugging Face Transformers library for efficient implementation of advanced models
• Compare different Transformers libraries to evaluate their suitability for specific tasks
• Develop a practical application of NLP that integrates RNN and attention mechanisms to solve real-world problems

Module 13. Autoencoders, GANs and Diffusion Models

• Develop efficient representations of data using Autoencoders, GANs and Diffusion Models
• Perform PCA using an incomplete linear autoencoder to optimize data representation
• Implement and understand the operation of stacked autoencoders
• Explore and apply convolutional autoencoders for efficient visual data representations
• Analyze and apply the effectiveness of sparse automatic encoders in data representation
• Generate fashion images from the MNIST dataset using Autoencoders
• Understand the concept of Generative Adversarial Networks (GANs) and Diffusion Models
• Implement and compare the performance of Diffusion Models and GANs in data generation

Module 14. Bio-Inspired Computing

• Introduce the fundamental concepts of bio-inspired computing.
• Analyze space exploration-exploitation strategies in genetic algorithms
• Examine models of evolutionary computation in the context of optimization
• Continue detailed analysis of evolutionary computation models
• Apply evolutionary programming to specific learning problems
• Address the complexity of multi-objective problems in the framework of bio-inspired computing
• Explore the application of neural networks in the field of bio-inspired computing
• Delve into the implementation and usefulness of neural networks in bio-inspired computing

Module 15. Artificial Intelligence: Strategies and Applications

• Develop strategies for the implementation of artificial intelligence in financial services
• Identify and assess the risks associated with the use of AI in the healthcare field
• Assess the potential risks associated with the use of AI in industry
• Apply Artificial Intelligence techniques in industry to improve productivity
• Design artificial intelligence solutions to optimize processes in public administration
• Evaluate the implementation of AI technologies in the education sector
• Apply artificial intelligence techniques in forestry and agriculture to improve productivity
• Optimize Human Resources processes through the strategic use of Artificial Intelligence

Module 16. Linguistic Models and AI Application

• Acquire a solid knowledge of the different linguistic models, from classical to AI-based, and their relevance in translation and interpreting
• Develop the skills to apply probabilistic, rule-based and deep learning models in Natural Language Processing (NLP) tasks

Module 17. AI and Real-Time Translation

• Learn to handle AI-based real-time translation tools, improving efficiency and accuracy in multilingual communication
• Develop skills to evaluate the quality of real-time translations, using specific metrics and indicators

Module 18. AI-Assisted Translation Tools and Platforms

• Familiarize yourself with the main AI-assisted translation tools and platforms (TAIA) and learn how to integrate them into your professional workflow
• Learn how to integrate linguistic resources and databases into TAIA tools, optimizing translation productivity and consistency

Module 19 Integration of Speech Recognition Technologies in Automatic Interpretation

• Develop skills to integrate speech recognition technologies into machine interpreting systems, improving the accessibility and quality of interpretations
• Learn how to improve the user experience in automatic interpreting systems through the optimization of speech recognition technologies

Module 20. Design of Multilanguage Interfaces and Chatbots Using AI Tools

• Acquire skills in the design and development of multilanguage chatbots using Artificial Intelligence, applying Natural Language Processing (NLP) techniques
• Learn to analyze data and optimize the performance of multilanguage chatbots, improving their interaction capacity in different contexts and platforms

You will be trained to lead and innovate in a highly technological and constantly evolving global environment, through the best teaching materials, at the forefront of technology and education”