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 Professional master’s degree is distinguished by its comprehensive approach, which will cover both traditional linguistic fundamentals and the application of advanced AI technologies. Therefore, professionals will acquire competencies to face contemporary challenges in translation and interpreting, learning to use AI tools and platforms that optimize these processes. In addition, it will include the mastery of emerging technologies, such as automatic interpretation and the development of multilingual chatbots, positioning the graduates at the forefront of technology and preparing them to lead in a digitized and globalized environment. 

This program will offer you a unique training, combining the classical knowledge of linguistics with the most recent innovations in Artificial Intelligence, supported by the revolutionary Relearning methodology” 

Module 1. Fundamentals of Artificial Intelligence

1.1. History of Artificial Intelligence

1.1.1. When Do We Start Talking About Artificial Intelligence? 
1.1.2. References in Film
1.1.3. Importance of Artificial Intelligence
1.1.4. Technologies that Enable and Support Artificial Intelligence

1.2. Artificial Intelligence in Games

1.2.1. Game Theory
1.2.2. Minimax and Alpha-Beta Pruning
1.2.3. Simulation: Monte Carlo

1.3. Neural Networks

1.3.1. Biological Fundamentals
1.3.2. Computational Model
1.3.3. Supervised and Unsupervised Neural Networks
1.3.4. Simple Perceptron
1.3.5. Multilayer Perceptron

1.4. Genetic Algorithms

1.4.1. History
1.4.2. Biological Basis
1.4.3. Problem Coding
1.4.4. Generation of the Initial Population
1.4.5. Main Algorithm and Genetic Operators
1.4.6. Evaluation of Individuals: Fitness

1.5. Thesauri, Vocabularies, Taxonomies

1.5.1. Vocabulary
1.5.2. Taxonomy
1.5.3. Thesauri
1.5.4. Ontologies
1.5.5. Knowledge Representation Semantic Web

1.6. Semantic Web

1.6.1. Specifications RDF, RDFS and OWL
1.6.2. Inference/ Reasoning
1.6.3. Linked Data

1.7. Expert Systems and DSS

1.7.1. Expert Systems
1.7.2. Decision Support Systems

1.8. Chatbots and Virtual Assistants 

1.8.1. Types of Assistants: Voice and Text Assistants 
1.8.2. Fundamental Parts for the Development of an Assistant: Intents, Entities and Dialog Flow
1.8.3. Integrations: Web, Slack, WhatsApp, Facebook
1.8.4. Assistant Development Tools: Dialog Flow, Watson Assistant 

1.9. AI Implementation Strategy
1.10. Future of Artificial Intelligence 

1.10.1. Understand How to Detect Emotions Using Algorithms 
1.10.2. Creating a Personality: Language, Expressions and Content 
1.10.3. Trends of Artificial Intelligence 
1.10.4. Reflections

Module 2. Data Types and Life Cycle

2.1. Statistics 

2.1.1. Statistics: Descriptive Statistics, Statistical Inferences 
2.1.2. Population, Sample, Individual 
2.1.3. Variables: Definition, Measurement Scales 

2.2. Types of Data Statistics 

2.2.1. According to Type 

2.2.1.1. Quantitative: Continuous Data and Discrete Data 
2.2.1.2. Qualitative. Binomial Data, Nominal Data and Ordinal Data

2.2.2. According to Its Shape  

2.2.2.1. Numeric 
2.2.2.2. Text:  
2.2.2.3. Logical 

2.2.3. According to Its Source 

2.2.3.1. Primary 
2.2.3.2. Secondary 

2.3. Life Cycle of Data 

2.3.1. Stages of the Cycle 
2.3.2. Milestones of the Cycle 
2.3.3. FAIR Principles 

2.4. Initial Stages of the Cycle 

2.4.1. Definition of Goals 
2.4.2. Determination of Resource Requirements 
2.4.3. Gantt Chart 
2.4.4. Data Structure 

2.5. Data Collection 

2.5.1. Methodology of Data Collection 
2.5.2. Data Collection Tools 
2.5.3. Data Collection Channels 

2.6. Data Cleaning 

2.6.1. Phases of Data Cleansing 
2.6.2. Data Quality 
2.6.3. Data Manipulation (with R) 

2.7. Data Analysis, Interpretation and Evaluation of Results 

2.7.1. Statistical Measures 
2.7.2. Relationship Indexes 
2.7.3. Data Mining 

2.8. Datawarehouse 

2.8.1. Elements that Comprise it 
2.8.2. Design 
2.8.3. Aspects to Consider 

2.9. Data Availability 

2.9.1. Access 
2.9.2. Uses 
2.9.3. Security 

Module 3. Data in Artificial Intelligence

3.1. Data Science

3.1.1. Data Science
3.1.2. Advanced Tools for Data Scientists

3.2. Data, Information and Knowledge

3.2.1. Data, Information and Knowledge 
3.2.2. Types of Data
3.2.3. Data Sources

3.3. From Data to Information 

3.3.1. Data Analysis
3.3.2. Types of Analysis
3.3.3. Extraction of Information from a Dataset

3.4. Extraction of Information Through Visualization

3.4.1. Visualization as an Analysis Tool
3.4.2. Visualization Methods 
3.4.3. Visualization of a Data Set

3.5. Data Quality

3.5.1. Quality Data
3.5.2. Data Cleaning 
3.5.3. Basic Data Pre-Processing

3.6. Dataset

3.6.1. Dataset Enrichment
3.6.2. The Curse of Dimensionality
3.6.3. Modification of Our Data Set

3.7. Unbalance 

3.7.1. Classes of Unbalance
3.7.2. Unbalance Mitigation Techniques
3.7.3. Balancing a Dataset

3.8. Unsupervised Models 

3.8.1. Unsupervised Model
3.8.2. Methods
3.8.3. Classification with Unsupervised Models

3.9. Supervised Models

3.9.1. Supervised Model
3.9.2. Methods
3.9.3. Classification with Supervised Models

3.10. Tools and Good Practices

3.10.1. Good Practices for Data Scientists
3.10.2. The Best Model 
3.10.3. Useful Tools

Module 4. Data Mining. Selection, Pre-Processing and Transformation

4.1. Statistical Inference

4.1.1. Descriptive Statistics vs. Statistical Inference
4.1.2. Parametric Procedures
4.1.3. Non-Parametric Procedures

4.2. Exploratory Analysis

4.2.1. Descriptive Analysis 
4.2.2. Visualization
4.2.3. Data Preparation

4.3. Data Preparation

4.3.1. Integration and Data Cleaning 
4.3.2. Normalization of Data
4.3.3. Transforming Attributes 

4.4. Missing Values

4.4.1. Treatment of Missing Values
4.4.2. Maximum Likelihood Imputation Methods
4.4.3. Missing Value Imputation Using Machine Learning

4.5. Noise in the Data 

4.5.1. Noise Classes and Attributes
4.5.2. Noise Filtering 
4.5.3. The Effect of Noise

4.6. The Curse of Dimensionality

4.6.1. Oversampling
4.6.2. Undersampling
4.6.3. Multidimensional Data Reduction

4.7. From Continuous to Discrete Attributes

4.7.1. Continuous Data Vs. Discreet Data
4.7.2. Discretization Process

4.8. The Data 

4.8.1. Data Selection 
4.8.2. Prospects and Selection Criteria
4.8.3. Selection Methods 

4.9. Instance Selection

4.9.1. Methods for Instance Selection
4.9.2. Prototype Selection
4.9.3. Advanced Methods for Instance Selection

4.10. Data Pre-processing in Big Data Environments

Module 5. Algorithm and Complexity in Artificial Intelligence

5.1. Introduction to Algorithm Design Strategies

5.1.1. Recursion
5.1.2. Divide and Conquer
5.1.3. Other Strategies

5.2. Efficiency and Analysis of Algorithms

5.2.1. Efficiency Measures
5.2.2. Measuring the Size of the Input
5.2.3. Measuring Execution Time
5.2.4. Worst, Best and Average Case
5.2.5. Asymptotic Notation
5.2.6. Criteria for Mathematical Analysis of Non-Recursive Algorithms
5.2.7. Mathematical Analysis of Recursive Algorithms
5.2.8. Empirical Analysis of Algorithms

5.3. Sorting Algorithms

5.3.1. Concept of Sorting
5.3.2. Bubble Sorting
5.3.3. Sorting by Selection
5.3.4. Sorting by Insertion
5.3.5. Merge Sort
5.3.6. Quick Sorting (Quick_Sort)

5.4. Algorithms with Trees

5.4.1. Tree Concept
5.4.2. Binary Trees
5.4.3. Tree Paths
5.4.4. Representing Expressions
5.4.5. Ordered Binary Trees
5.4.6. Balanced Binary Trees

5.5. Algorithms Using Heaps

5.5.1. Heaps
5.5.2. The Heapsort Algorithm
5.5.3. Priority Queues

5.6. Graph Algorithms

5.6.1. Representation
5.6.2. Traversal in Width
5.6.3. Depth Travel
5.6.4. Topological Sorting

5.7. Greedy Algorithms

5.7.1. Greedy Strategy
5.7.2. Elements of the Greedy Strategy
5.7.3. Currency Exchange
5.7.4. Traveler’s Problem
5.7.5. Backpack Problem

5.8. Minimal Path Finding

5.8.1. The Minimum Path Problem
5.8.2. Negative Arcs and Cycles
5.8.3. Dijkstra's Algorithm

5.9. Greedy Algorithms on Graphs

5.9.1. The Minimum Covering Tree
5.9.2. Prim's Algorithm
5.9.3. Kruskal’s Algorithm
5.9.4. Complexity Analysis

5.10. Backtracking

5.10.1. Backtracking
5.10.2. Alternative Techniques

Module 6. Intelligent Systems

6.1. Agent Theory

6.1.1. Concept History
6.1.2. Agent Definition
6.1.3. Agents in Artificial Intelligence
6.1.4. Agents in Software Engineering

6.2. Agent Architectures

6.2.1. The Reasoning Process of an Agent
6.2.2. Reactive Agents
6.2.3. Deductive Agents
6.2.4. Hybrid Agents
6.2.5. Comparison

6.3. Information and Knowledge

6.3.1. Difference between Data, Information and Knowledge
6.3.2. Data Quality Assessment
6.3.3. Data Collection Methods
6.3.4. Information Acquisition Methods
6.3.5. Knowledge Acquisition Methods

6.4. Knowledge Representation

6.4.1. The Importance of Knowledge Representation
6.4.2. Definition of Knowledge Representation According to Roles
6.4.3. Knowledge Representation Features

6.5. Ontologies

6.5.1. Introduction to Metadata
6.5.2. Philosophical Concept of Ontology
6.5.3. Computing Concept of Ontology
6.5.4. Domain Ontologies and Higher-Level Ontologies
6.5.5. How to Build an Ontology

6.6. Ontology Languages and Ontology Creation Software 

6.6.1. Triple RDF, Turtle and N
6.6.2. RDF Schema
6.6.3. OWL
6.6.4. SPARQL
6.6.5. Introduction to Ontology Creation Tools
6.6.6. Installing and Using Protégé

6.7. Semantic Web

6.7.1. Current and Future Status of the Semantic Web
6.7.2. Semantic Web Applications

6.8. Other Knowledge Representation Models

6.8.1. Vocabulary
6.8.2. Global Vision
6.8.3. Taxonomy
6.8.4. Thesauri
6.8.5. Folksonomy
6.8.6. Comparison
6.8.7. Mind Maps

6.9. Knowledge Representation Assessment and Integration

6.9.1. Zero-Order Logic
6.9.2. First-Order Logic
6.9.3. Descriptive Logic
6.9.4. Relationship between Different Types of Logic
6.9.5. Prolog: Programming Based on First-Order Logic

6.10. Semantic Reasoners, Knowledge-Based Systems and Expert Systems

6.10.1. Concept of Reasoner
6.10.2. Reasoner Applications
6.10.3. Knowledge-Based Systems
6.10.4. MYCIN: History of Expert Systems
6.10.5. Expert Systems Elements and Architecture
6.10.6. Creating Expert Systems

Module 7. Machine Learning and Data Mining

7.1. Introduction to Knowledge Discovery Processes and Basic Concepts of Machine Learning

7.1.1. Key Concepts of Knowledge Discovery Processes
7.1.2. Historical Perspective of Knowledge Discovery Processes
7.1.3. Stages of the Knowledge Discovery Processes
7.1.4. Techniques Used in Knowledge Discovery Processes
7.1.5. Characteristics of Good Machine Learning Models
7.1.6. Types of Machine Learning Information
7.1.7. Basic Learning Concepts
7.1.8. Basic Concepts of Unsupervised Learning

7.2. Data Exploration and Pre-processing

7.2.1. Data Processing
7.2.2. Data Processing in the Data Analysis Flow
7.2.3. Types of Data
7.2.4. Data Transformations
7.2.5. Visualization and Exploration of Continuous Variables
7.2.6. Visualization and Exploration of Categorical Variables
7.2.7. Correlation Measures
7.2.8. Most Common Graphic Representations
7.2.9. Introduction to Multivariate Analysis and Dimensionality Reduction

7.3. Decision Trees

7.3.1. ID Algorithm
7.3.2. Algorithm C
7.3.3. Overtraining and Pruning
7.3.4. Result Analysis

7.4. Evaluation of Classifiers

7.4.1. Confusion Matrixes
7.4.2. Numerical Evaluation Matrixes
7.4.3. Kappa Statistic
7.4.4. ROC Curves

7.5. Classification Rules

7.5.1. Rule Evaluation Measures
7.5.2. Introduction to Graphic Representation
7.5.3. Sequential Overlay Algorithm

7.6. Neural Networks

7.6.1. Basic Concepts
7.6.2. Simple Neural Networks
7.6.3. Backpropagation Algorithm
7.6.4. Introduction to Recurrent Neural Networks

7.7. Bayesian Methods

7.7.1. Basic Probability Concepts
7.7.2. Bayes' Theorem
7.7.3. Naive Bayes
7.7.4. Introduction to Bayesian Networks

7.8. Regression and Continuous Response Models

7.8.1. Simple Linear Regression
7.8.2. Multiple Linear Regression
7.8.3. Logistic Regression
7.8.4. Regression Trees
7.8.5. Introduction to Support Vector Machines (SVM)
7.8.6. Goodness-of-Fit Measures

7.9. Clustering

7.9.1. Basic Concepts
7.9.2. Hierarchical Clustering
7.9.3. Probabilistic Methods
7.9.4. EM Algorithm
7.9.5. B-Cubed Method
7.9.6. Implicit Methods

7.10. Text Mining and Natural Language Processing (NLP)

7.10.1. Basic Concepts
7.10.2. Corpus Creation
7.10.3. Descriptive Analysis
7.10.4. Introduction to Feelings Analysis

Module 8. Neural Networks, the Basis of Deep Learning

8.1. Deep Learning

8.1.1. Types of Deep Learning
8.1.2. Applications of Deep Learning
8.1.3. Advantages and Disadvantages of Deep Learning

8.2. Surgery

8.2.1. Sum
8.2.2. Product
8.2.3. Transfer

8.3. Layers

8.3.1. Input Layer
8.3.2. Hidden Layer
8.3.3. Output Layer

8.4. Layer Bonding and Operations

8.4.1. Architecture Design
8.4.2. Connection between Layers
8.4.3. Forward Propagation

8.5. Construction of the First Neural Network

8.5.1. Network Design
8.5.2. Establish the Weights
8.5.3. Network Training

8.6. Trainer and Optimizer

8.6.1. Optimizer Selection
8.6.2. Establishment of a Loss Function
8.6.3. Establishing a Metric

8.7. Application of the Principles of Neural Networks

8.7.1. Activation Functions
8.7.2. Backward Propagation
8.7.3. Parameter Adjustment

8.8. From Biological to Artificial Neurons

8.8.1. Functioning of a Biological Neuron
8.8.2. Transfer of Knowledge to Artificial Neurons
8.8.3. Establish Relations Between the Two

8.9. Implementation of MLP (Multilayer Perceptron) with Keras

8.9.1. Definition of the Network Structure
8.9.2. Model Compilation
8.9.3. Model Training

8.10. Fine Tuning Hyperparameters of Neural Networks

8.10.1. Selection of the Activation Function
8.10.2. Set the Learning Rate
8.10.3. Adjustment of Weights

Module 9. Deep Neural Networks Training

9.1. Gradient Problems

9.1.1. Gradient Optimization Techniques
9.1.2. Stochastic Gradients
9.1.3. Weight Initialization Techniques

9.2. Reuse of Pre-Trained Layers

9.2.1. Learning Transfer Training
9.2.2. Feature Extraction
9.2.3. Deep Learning

9.3. Optimizers

9.3.1. Stochastic Gradient Descent Optimizers
9.3.2. Optimizers Adam and RMSprop
9.3.3. Moment Optimizers

9.4. Learning Rate Programming

9.4.1. Automatic Learning Rate Control
9.4.2. Learning Cycles
9.4.3. Smoothing Terms

9.5. Overfitting

9.5.1. Cross Validation
9.5.2. Regularization
9.5.3. Evaluation Metrics

9.6. Practical Guidelines

9.6.1. Model Design
9.6.2. Selection of Metrics and Evaluation Parameters
9.6.3. Hypothesis Testing

9.7. Transfer Learning

9.7.1. Learning Transfer Training
9.7.2. Feature Extraction
9.7.3. Deep Learning

9.8. Data Augmentation

9.8.1. Image Transformations
9.8.2. Synthetic Data Generation
9.8.3. Text Transformation

9.9. Practical Application of Transfer Learning

9.9.1. Learning Transfer Training
9.9.2. Feature Extraction
9.9.3. Deep Learning

9.10. Regularization

9.10.1. L and L
9.10.2. Regularization by Maximum Entropy
9.10.3. Dropout

Module 10. Model Customization and Training with TensorFlow

10.1. TensorFlow

10.1.1. Use of the TensorFlow Library
10.1.2. Model Training with TensorFlow
10.1.3. Operations with Graphs in TensorFlow

10.2. TensorFlow and NumPy

10.2.1. NumPy Computing Environment for TensorFlow
10.2.2. Using NumPy Arrays with TensorFlow
10.2.3. NumPy Operations for TensorFlow Graphs

10.3. Model Customization and Training Algorithms

10.3.1. Building Custom Models with TensorFlow
10.3.2. Management of Training Parameters
10.3.3. Use of Optimization Techniques for Training

10.4. TensorFlow Features and Graphs

10.4.1. Functions with TensorFlow
10.4.2. Use of Graphs for Model Training
10.4.3. Grap Optimization with TensorFlow Operations

10.5. Loading and Preprocessing Data with TensorFlow

10.5.1. Loading Data Sets with TensorFlow
10.5.2. Preprocessing Data with TensorFlow
10.5.3. Using TensorFlow Tools for Data Manipulation

10.6. The tfdata API

10.6.1. Using the tf.data API for Data Processing
10.6.2. Construction of Data Streams with tf.data
10.6.3. Using the tf.data API for Model Training

10.7. The TFRecord Format

10.7.1. Using the TFRecord API for Data Serialization
10.7.2. TFRecord File Upload with TensorFlow
10.7.3. Using TFRecord Files for Model Training

10.8. Keras Preprocessing Layers

10.8.1. Using the Keras Preprocessing API
10.8.2. Preprocessing Pipelined Construction with Keras
10.8.3. Using the Keras Preprocessing API for Model Training

10.9. The TensorFlow Datasets Project

10.9.1. Using TensorFlow Datasets for Data Loading
10.9.2. Data Preprocessing with TensorFlow Datasets
10.9.3. Using TensorFlow Datasets for Model Training

10.10. Building a Deep Learning App with TensorFlow

10.10.1. Practical Applications
10.10.2. Building a Deep Learning App with TensorFlow
10.10.3. Model Training with TensorFlow
10.10.4. Use of the Application for the Prediction of Results

Module 11. Deep Computer Vision with Convolutional Neural Networks

11.1. The Visual Cortex Architecture

11.1.1. Functions of the Visual Cortex
11.1.2. Theories of Computational Vision
11.1.3. Models of Image Processing

11.2. Convolutional Layers

11.2.1. Reuse of Weights in Convolution
11.2.2. Convolution D
11.2.3. Activation Functions

11.3. Grouping Layers and Implementation of Grouping Layers with Keras

11.3.1. Pooling and Striding
11.3.2. Flattening
11.3.3. Types of Pooling

11.4. CNN Architecture

11.4.1. VGG Architecture
11.4.2. AlexNet Architecture
11.4.3. ResNet Architecture

11.5. Implementing a CNN ResNet- using Keras

11.5.1. Weight Initialization
11.5.2. Input Layer Definition
11.5.3. Output Definition

11.6. Use of Pre-trained Keras Models

11.6.1. Characteristics of Pre-Trained Models
11.6.2. Uses of Pre-Trained Models
11.6.3. Advantages of Pre-Trained Models

11.7. Pre-Trained Models for Transfer Learning

11.7.1. Transfer Learning
11.7.2. Transfer Learning Process
11.7.3. Advantages of Transfer Learning

11.8. Deep Computer Vision Classification and Localization

11.8.1. Image Classification
11.8.2. Localization of Objects in Images
11.8.3. Object Detection

11.9. Object Detection and Object Tracking

11.9.1. Object Detection Methods
11.9.2. Object Tracking Algorithms
11.9.3. Tracking and Localization Techniques

11.10. Semantic Segmentation

11.10.1. Deep Learning for Semantic Segmentation
11.10.1. Edge Detection
11.10.1. Rule-Based Segmentation Methods

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

12.1. Text Generation using RNN

12.1.1. Training an RNN for Text Generation
12.1.2. Natural Language Generation with RNN
12.1.3. Text Generation Applications with RNN

12.2. Training Data Set Creation

12.2.1. Preparation of the Data for Training an RNN
12.2.2. Storage of the Training Dataset
12.2.3. Data Cleaning and Transformation
12.2.4. Sentiment Analysis

12.3. Classification of Opinions with RNN

12.3.1. Detection of Themes in Comments
12.3.2. Sentiment Analysis with Deep Learning Algorithms

12.4. Encoder-Decoder Network for Neural Machine Translation

12.4.1. Training an RNN for Machine Translation
12.4.2. Use of an Encoder-Decoder Network for Machine Translation
12.4.3. Improving the Accuracy of Machine Translation with RNNs

12.5. Attention Mechanisms

12.5.1. Application of Care Mechanisms in RNN
12.5.2. Use of Care Mechanisms to Improve the Accuracy of the Models
12.5.3. Advantages of Attention Mechanisms in Neural Networks

12.6. Transformer Models

12.6.1. Using Transformers Models for Natural Language Processing
12.6.2. Application of Transformers Models for Vision
12.6.3. Advantages of Transformers Models

12.7. Transformers for Vision

12.7.1. Use of Transformers Models for Vision
12.7.2. Image Data Preprocessing
12.7.3. Training a Transformers Model for Vision

12.8. Hugging Face’s Transformers Library

12.8.1. Using Hugging Face's Transformers Library
12.8.2. Hugging Face’s Transformers Library Application
12.8.3. Advantages of Hugging Face’s Transformers Library

12.9. Other Transformers Libraries. Comparison

12.9.1. Comparison Between Different Transformers Libraries
12.9.2. Use of the Other Transformers Libraries
12.9.3. Advantages of the Other Transformers Libraries

12.10. Development of an NLP Application with RNN and Attention. Practical Application

12.10.1. Development of a Natural Language Processing Application with RNN and Attention.
12.10.2. Use of RNN, Attention Mechanisms and Transformers Models in the Application
12.10.3. Evaluation of the Practical Application

Module 13. Autoencoders, GANs and Diffusion Models

13.1. Representation of Efficient Data

13.1.1. Dimensionality Reduction
13.1.2. Deep Learning
13.1.3. Compact Representations

13.2. PCA Realization with an Incomplete Linear Automatic Encoder

13.2.1. Training Process
13.2.2. Implementation in Python
13.2.3. Use of Test Data

13.3. Stacked Automatic Encoders

13.3.1. Deep Neural Networks
13.3.2. Construction of Coding Architectures
13.3.3. Use of Regularization

13.4. Convolutional Autoencoders

13.4.1. Design of Convolutional Models
13.4.2. Convolutional Model Training
13.4.3. Results Evaluation

13.5. Noise Suppression of Automatic Encoders

13.5.1. Filter Application
13.5.2. Design of Coding Models
13.5.3. Use of Regularization Techniques

13.6. Sparse Automatic Encoders

13.6.1. Increasing Coding Efficiency
13.6.2. Minimizing the Number of Parameters
13.6.3. Using Regularization Techniques

13.7. Variational Automatic Encoders

13.7.1. Use of Variational Optimization
13.7.2. Unsupervised Deep Learning
13.7.3. Deep Latent Representations

13.8. Generation of Fashion MNIST Images

13.8.1. Pattern Recognition
13.8.2. Image Generation
13.8.3. Deep Neural Networks Training

13.9. Generative Adversarial Networks and Diffusion Models

13.9.1. Content Generation from Images
13.9.2. Modeling of Data Distributions
13.9.3. Use of Adversarial Networks

13.10. Implementation of the Models

13.10.1. Practical Applications
13.10.2. Implementation of the Models
13.10.3. Use of Real Data
13.10.4. Results Evaluation

Module 14. Bio-Inspired Computing 

14.1. Introduction to Bio-Inspired Computing

14.1.1. Introduction to Bio-Inspired Computing

14.2. Social Adaptation Algorithms

14.2.1. Bio-Inspired Computation Based on Ant Colonies
14.2.2. Variants of Ant Colony Algorithms
14.2.3. Particle Cloud Computing

14.3. Genetic Algorithms

14.3.1. General Structure
14.3.2. Implementations of the Major Operators

14.4. Space Exploration-Exploitation Strategies for Genetic Algorithms

14.4.1. CHC Algorithm
14.4.2. Multimodal Problems

14.5. Evolutionary Computing Models (I)

14.5.1. Evolutionary Strategies
14.5.2. Evolutionary Programming
14.5.3. Algorithms Based on Differential Evolution

14.6. Evolutionary Computation Models (II)

14.6.1. Evolutionary Models Based on Estimation of Distributions (EDA)
14.6.2. Genetic Programming

14.7. Evolutionary Programming Applied to Learning Problems

14.7.1. Rules-Based Learning
14.7.2. Evolutionary Methods in Instance Selection Problems

14.8. Multi-Objective Problems

14.8.1. Concept of Dominance
14.8.2. Application of Evolutionary Algorithms to Multi-Objective Problems

14.9. Neural Networks (I)

14.9.1. Introduction to Neural Networks
14.9.2. Practical Example with Neural Networks

14.10. Neural Networks (II)

14.10.1. Use Cases of Neural Networks in Medical Research
14.10.2. Use Cases of Neural Networks in Economics
14.10.3. Use Cases of Neural Networks in Artificial Vision

Module 15. Artificial Intelligence: Strategies and Applications

15.1. Financial Services

15.1.1. The Implications of Artificial Intelligence in Financial Services. Opportunities and Challenges
15.1.2. Case Uses 
15.1.3. Potential Risks Related to the Use of Artificial Intelligence
15.1.4. Potential Future Developments / Uses of Artificial Intelligence

15.2. Implications of Artificial Intelligence in Healthcare Service 

15.2.1. Implications of Artificial Intelligence in the Healthcare Sector. Opportunities and Challenges 
15.2.2. Case Uses

15.3. Risks Related to the Use of Artificial Intelligence in Health Services

15.3.1. Potential Risks Related to the Use of Artificial Intelligence
15.3.2. Potential Future Developments / Uses of Artificial Intelligence

15.4. Retail 

15.4.1. Implications of Artificial Intelligence in Retail. Opportunities and Challenges 
15.4.2. Case Uses 
15.4.3. Potential Risks Related to the Use of Artificial Intelligence 
15.4.4. Potential Future Developments / Uses of Artificial Intelligence

15.5. Industry 

15.5.1. Implications of Artificial Intelligence in Industry. Opportunities and Challenges
15.5.2. Case Uses

15.6. Potential Risks Related to the Use of Artificial Intelligence in the Industry  

15.6.1. Case Uses
15.6.2. Potential Risks Related to the Use of Artificial Intelligence
15.6.3. Potential Future Developments / Uses of Artificial Intelligence

15.7. Public Administration 

15.7.1. Implications of Artificial Intelligence in Public Administration. Opportunities and Challenges
15.7.2. Case Uses 
15.7.3. Potential Risks Related to the Use of Artificial Intelligence 
15.7.4. Potential Future Developments / Uses of Artificial Intelligence

15.8. Educational 

15.8.1. Implications of Artificial Intelligence in Education. Opportunities and Challenges
15.8.2. Case Uses 
15.8.3. Potential Risks Related to the Use of Artificial Intelligence 
15.8.4. Potential Future Developments / Uses of Artificial Intelligence

15.9. Forestry and Agriculture 

15.9.1. Implications of Artificial Intelligence in Forestry and Agriculture. Opportunities and Challenges 
15.9.2. Case Uses
15.9.3. Potential Risks Related to the Use of Artificial Intelligence
15.9.4. Potential Future Developments / Uses of Artificial Intelligence

15.10. Human Resources 

15.10.1. Implications of Artificial Intelligence in Human Resources. Opportunities and Challenges
15.10.2. Case Uses 
15.10.3. Potential Risks Related to the Use of Artificial Intelligence
15.10.4. Potential Future Developments / Uses of Artificial Intelligence

Module 16. Linguistic Models and AI Application 

16.1. Classical Models of Linguistics and their Relevance to AI 

16.1.1. Generative and Transformational Grammar 
16.1.2. Structural Linguistic Theory 
16.1.3. Formal Grammar Theory 
16.1.4. Applications of Classical Models in AI 

16.2. Probabilistic Models in Linguistics and Their Application in AI 

16.2.1. Hidden Markov Models (HMM) 
16.2.2. Statistical Language Models 
16.2.3. Supervised and Unsupervised Learning Algorithms 
16.2.4. Applications in Speech Recognition and Text Processing 

16.3. Rule-Based Models and Their Implementation in AI. GPT 

16.3.1. Formal Grammars and Rule Systems 
16.3.2. Knowledge Representation and Computational Logic 
16.3.3. Expert Systems and Inference Engines 
16.3.4. Applications in Dialog Systems and Virtual Assistants 

16.4. Deep Learning Models in Linguistics and Their Use in AI 

16.4.1. Convolutional Neural Networks for Text Processing 
16.4.2. Recurrent Neural Networks and LSTM for Sequence Modeling 
16.4.3. Attention Models and Transformers. APERTIUM 
16.4.4. Applications in Machine Translation, Text Generation and Sentiment Analysis.  

16.5. Distributed Language Representations and Their Impact on AI 

16.5.1. Word Embeddings and Vector Space Models 
16.5.2. Distributed Representations of Sentences and Documents 
16.5.3. Bag-of-Words Models and Continuous Language Models 
16.5.4. Applications in Information Retrieval, Document Clustering and Content Recommendation  

16.6. Machine Translation Models and Their Evolution in AI. Lilt 

16.6.1. Statistical and Rule-Based Translation Models 
16.6.2. Advances in Neural Machine Translation 
16.6.3. Hybrid Approaches and Multilingual Models 
16.6.4. Applications in Online Translation and Content Localization Services 

16.7. Sentiment Analysis Models and Their Usefulness in AI 

16.7.1. Sentiment Classification Methods 
16.7.2. Detection of Emotions in Text 
16.7.3. Analysis of User Opinions and Comments 
16.7.4. Applications in Social Networks, Analysis of Product Opinions and Customer Service  

16.8. Language Generation Models and Their Application in AI. TransPerfect Globallink 

16.8.1. Autoregressive Text Generation Models 
16.8.2. Conditioned and Controlled Text Generation 
16.8.3. GPT-Based Natural Language Generation Models 
16.8.4. Applications in Automatic Typing, Text Summarization, and Intelligent Conversation 

16.9. Speech Recognition Models and Their Integration in AI 

16.9.1. Audio Feature Extraction Methods 
16.9.2. Speech Recognition Models Based on Neural Networks 
16.9.3. Improvements in Speech Recognition Accuracy and Robustness 
16.9.4. Applications in Virtual Assistants, Transcription Systems and Speech-based Device Control 

16.10. Challenges and Future of Linguistic Models in AI 

16.10.1. Challenges in Natural Language Understanding 
16.10.2. Limitations and Biases in Current Linguistic Models 
16.10.3. Research and Future Trends in AI Linguistic Modeling 
16.10.4. Impact on Future Applications such as General Artificial Intelligence (AGI) and Human Language Understanding. SmartCAt 

Module 17. AI and Real-Time Translation 

17.1. Introduction to Real-Time Translation with AI 

17.1.1. Definition and Basic Concepts 
17.1.2. Importance and Applications in Different Contexts 
17.1.3. Challenges and Opportunities 
17.1.4. Tools such as Fluently or Voice Tra 

17.2. Artificial Intelligence Fundamentals in Translation 

17.2.1. Brief Introduction to Artificial Intelligence 
17.2.2. Specific Applications in Translation 
17.2.3. Relevant Models and Algorithms 

17.3. AI-Based Real-Time Translation Tools 

17.3.1. Description of the Main Tools Available 
17.3.2. Comparison of Functionalities and Features 
17.3.3. Use Cases and Practical Examples 

17.4. Neural Machine Translation (NMT) Models. SDL Language Cloud 

17.4.1. Principles and Operation of NMT Models 
17.4.2. Advantages over Traditional Approaches 
17.4.3. Development and Evolution of NMT Models 

17.5. Natural Language Processing (NLP) in Real-Time Translation. SayHi TRanslate 

17.5.1. Basic NLP Concepts Relevant to Translation 
17.5.2. Preprocessing and Post-Processing Techniques 
17.5.3. Improving the Coherence and Cohesion of the Translated Text 

17.6. Multilingual and Multimodal Translation Models 

17.6.1. Translation Models that Support Multiple Languages 
17.6.2. Integration of Modalities such as Text, Speech and Images 
17.6.3. Challenges and Considerations in Multilingual and Multimodal Translation 

17.7. Quality Assessment in Real-Time Translation with AI 

17.7.1. Translation Quality Assessment Metrics 
17.7.2. Automatic and Human Evaluation Methods. iTranslate Voice 
17.7.3. Strategies to Improve Translation Quality 

17.8. Integration of Real-Time Translation Tools in Professional Environments 

17.8.1. Use of Translation Tools in Daily Work 
17.8.2. Integration with Content Management and Localization Systems 
17.8.3. Adaptation of Tools to Specific User Needs 

17.9. Ethical and Social Challenges in Real-Time Translation with AI 

17.9.1. Biases and Discrimination in Machine Translation 
17.9.2. Privacy and Security of User Data 
17.9.3. Impact on Linguistic and Cultural Diversity 

17.10. Future of AI-Based Real-Time Translation. Applingua 

17.10.1. Emerging Trends and Technological Advances 
17.10.2. Future Prospects and Potential Innovative Applications 
17.10.3. Implications for Global Communication and Language Accessibility 

Module 18. AI-Assisted Translation Tools and Platforms 

18.1. Introduction to AI-Assisted Translation Tools and Platforms 

18.1.1. Definition and Basic Concepts 
18.1.2. Brief History and Evolution 
18.1.3. Importance and Benefits in Professional Translation 

18.2. Main AI-Assisted Translation Tools 

18.2.1. Description and Functionalities of the Leading Tools on the Market 
18.2.2. Comparison of Features and Prices 
18.2.3. Use Cases and Practical Examples 

18.3. Professional AI-Assisted Translation Platforms. Wordfast 

18.3.1. Description of Popular AI-Assisted Translation Platforms 
18.3.2. Specific Functionalities for Translation Teams and Agencies 
18.3.3. Integration with Other Project Management Systems and Tools 

18.4. Machine Translation Models Implemented in AI-Assisted Translation Tools 

18.4.1. Statistical Translation Models 
18.4.2. Neural Translation Models 
18.4.3. Advances in Neural Machine Translation (NMT) and Its Impact on AI-Assisted Translation Tools 

18.5. Integration of Linguistic Resources and Databases in AI-Assisted Translation Tools 

18.5.1. Using Corpus and Linguistic Databases to Improve Translation Accuracy  
18.5.2. Integrating Specialized Dictionaries and Glossaries 
18.5.3. Importance of Context and Specific Terminology in AI-Assisted Translation 

18.6. User Interface and User Experience in AI-Assisted Translation Tools 

18.6.1. User Interface Design and Usability 
18.6.2. Customization and Preference Settings 
18.6.3. Accessibility and Multilingual Support on AI-Assisted Translation Platforms 

18.7. Quality Assessment in AI-Assisted Translation 

18.7.1. Translation Quality Assessment Metrics 
18.7.2. Machine vs. Human Evaluation 
18.7.3. Strategies to Improve the Quality of AI-Assisted Translation 

18.8. Integration of AI-Assisted Translation Tools into the Translator's Workflow 

18.8.1. Incorporation of AI-Assisted Translation Tools into the Translation Process 
18.8.2. Optimizing Workflow and Increasing Productivity 
18.8.3. Collaboration and Teamwork in AI-Assisted Translation Environments 

18.9. Ethical and Social Challenges in the Use of AI-Assisted Translation Tools 

18.9.1. Biases and Discrimination in Machine Translation 
18.9.2. Privacy and Security of User Data 
18.9.3. Impact on the Translation Profession and on Linguistic and Cultural Diversity 

18.10. Future of AI-Assisted Translation Tools and Platforms.Wordbee 

18.10.1. Emerging Trends and Technological Developments 
18.10.2. Future Prospects and Potential Innovative Applications 
18.10.3. Implications for Training and Professional Development in the Field of Translation 

Module 19. Integration of Speech Recognition Technologies in Machine Interpreting 

19.1. Introduction to the Integration of Speech Recognition Technologies in Machine Interpreting 

19.1.1. Definition and Basic Concepts 
19.1.2. Brief History and Evolution. Kaldi 
19.1.3. Importance and Benefits in the Field of Interpretation 

19.2. Principles of Speech Recognition for Machine Interpreting 

19.2.1. How Speech Recognition Works 
19.2.2. Technologies and Algorithms Used 
19.2.3. Types of Speech Recognition Systems 

19.3. Development and Improvements in Speech Recognition Technologies 

19.3.1. Recent Technological Advances. Speech Recognition 
19.3.2. Improvements in Accuracy and Speed 
19.3.3. Adaptation to Different Accents and Dialects 

19.4. Speech Recognition Platforms and Tools for Machine Interpreting 

19.4.1. Description of the Main Platforms and Tools Available 
19.4.2. Comparison of Functionalities and Features 
19.4.3. Use Cases and Practical Examples. Speechmatics 

19.5. Integrating Speech Recognition Technologies into Machine Interpreting Systems 

19.5.1. Design and Implementation of Machine Interpreting Systems with Speech Recognition 
19.5.2. Adaptation to Different Interpreting Environments and Situations 
19.5.3. Technical and Infrastructure Considerations 

19.6. Optimization of the User Experience in Machine Interpreting with Speech Recognition  

19.6.1. Design of Intuitive and Easy to Use User Interfaces 
19.6.2. Customization and Configuration of Preferences. OTTER.ai 
19.6.3. Accessibility and Multilingual Support in Machine Interpreting Systems 

19.7. Assessment of the Quality in Machine Interpreting with Speech Recognition 

19.7.1. Interpretation Quality Assessment Metrics 
19.7.2. Machine vs. Human Evaluation 
19.7.3. Strategies to Improve the Quality in Machine Interpreting with Speech Recognition 

19.8. Ethical and Social Challenges in the Use of Speech Recognition Technologies in Machine Interpreting  

19.8.1. Privacy and Security of User Data 
19.8.2. Biases and Discrimination in Speech Recognition 
19.8.3. Impact on the Interpreting Profession and on Linguistic and Cultural Diversity 

19.9. Specific Applications of Machine Interpreting with Speech Recognition 

19.9.1. Real-Time Interpreting in Business and Commercial Environments 
19.9.2. Remote and Telephonic Interpreting with Speech Recognition 
19.9.3. Interpreting at International Events and Conferences 

19.10. Future of the Integration of Speech Recognition Technologies in Machine Interpreting 

19.10.1. Emerging Trends and Technological Developments. CMU Sphinx 
19.10.2. Future Prospects and Potential Innovative Applications 
19.10.3. Implications for Global Communication and Elimination of Language Barriers

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

20.1. Fundamentals of Multilanguage Interfaces 

20.1.1. Design Principles for Multilingualism: Usability and Accessibility with AI 
20.1.2. Key Technologies: Using TensorFlow and PyTorch for Interface Development 
20.1.3. Case Studies: Analysis of Successful Interfaces Using AI 

20.2. Introduction to Chatbots with AI 

20.2.1. Evolution of Chatbots: from Simple to AI-Driven 
20.2.2. Comparison of Chatbots: Rules vs. AI-Based Models 
20.2.3. Components of AI-Driven Chatbots: Use of Natural Language Understanding (NLU) 

20.3. Multilanguage Chatbot Architectures with AI 

20.3.1. Design of Scalable Architectures with IBM Watson 
20.3.2. Designing Scalable Architectures with IBM Watson 
20.3.3. Integration of Chatbots in Platforms with Microsoft Bot Framework 

20.4. Natural Language Processing (NLP) for Chatbots 

20.4.1. Syntactic and Semantic Parsing with Google BERT 
20.4.2. Language Model Training with OpenAI GPT 
20.4.3. Application of PLN Tools such as spaCy in Chatbots 

20.5. Development of Chatbots with AI Frameworks 

20.5.1. Implementation with Google Dialogflow 
20.5.2. Creating and Training Dialog Flows with IBM Watson 
20.5.3. Advanced Customization Using AI APIs such as Microsoft LUIS 

20.6. Conversation and Context Management in Chatbots 

20.6.1. State Models with Rasa for Chatbots 
20.6.2. Conversational Management Strategies with Deep Learning 
20.6.3. Real-Time Ambiguity Resolution and Corrections Using AI 

20.7. UX/UI Design for Multilanguage Chatbots with AI 

20.7.1. User-Centered Design Using AI Data Analytics 
20.7.2. Cultural Adaptation with Automatic Localization Tools 
20.7.3. Usability Testing with AI-Based Simulations 

20.8. Integration of Multi-Channel Chatbots with AI 

20.8.1. Omni-Channel Development with TensorFlow 
20.8.2. Secure and Private Integration Strategies with AI Technologies 
20.8.3. Security Considerations with AI Cryptography Algorithms 

20.9. Data Analysis and Chatbot Optimization 

20.9.1. Use of Analytics Platforms such as Google Analytics for Chatbots 
20.9.2. Performance Optimization with Machine Learning Algorithms 
20.9.3. Machine Learning for Continuous Chatbot Refinement 

20.10. Implementing a Multilanguage Chatbot with AI 

20.10.1. Project Definition with AI Management Tools 
20.10.2. Technical Implementation Using TensorFlow or PyTorch 
20.10.3. Evaluation and Tuning Based on Machine Learning and User Feedback

You will equip yourself with skills to face contemporary challenges in translation and interpreting by learning how to use AI tools and platforms that optimize these processes”