With this Master's Degree you will discover how artificial intelligence is transforming industries and you will prepare yourself to lead the change" 

AI is transforming numerous industries, from healthcare to logistics to automotive to e-commerce. Its ability to automate repetitive tasks and improve efficiency has generated a growing demand for professionals capable of mastering different types of machine learning algorithms. In such a new and constantly evolving sector, it is imperative to stay up-to-date in order to compete in an increasingly technology-driven
job market. 

Precisely for this reason, TECH Global University has developed a program that is presented as a strategic response to improve the job prospects and promotion potential of students. In this way, it has developed an innovative syllabus in which students will delve into the fundamentals of AI and deepen their knowledge of text mining.

Throughout the development of this Master's Degree, students will dive into the essential fundamentals, tracing the historical evolution of AI and exploring its future projections. In this way, they will delve into the integration of AI in mass-use applications to understand how these platforms improve user experience and optimize operational efficiency. This is an exclusive academic program with which students will be able to develop optimization processes inspired by biological evolution, finding and applying efficient solutions to complex problems with an in-depth mastery of AI.

And to facilitate the integration of new knowledge, TECH has created this complete program based on the exclusive Relearningmethodology. Under this approach, students will reinforce understanding through repetition of key concepts throughout the program, which will be presented in various audiovisual supports for a progressive and effective knowledge acquisition. All of this is presented in an innovative and flexible fully online system that allows students to adapt learning to their schedules.

Boost your professional profile by developing advanced AI-based solutions with the most comprehensive program in the digital academic landscape" 

This Master's Degree in Artificial Intelligence contains the most complete and up-to-date scientific program on the market. The most important features include:

• Development of practical cases presented by experts in Artificial Intelligence 
• The graphic, schematic and eminently practical contents of the book provide updated and practical information on those disciplines that are essential for professional practice 
• Practical exercises where self-assessment can be used 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 cover from the evolution of neural networks to Deep Learning and acquire solid skills in the implementation of advanced Artificial Intelligence solutions with the TECH seal of quality" 

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

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

The design of this program focuses on Problem-Based Learning, by means of which the professionals must try to solve the different professional practice situations that are presented throughout the academic course. For this purpose, the students will be assisted by an innovative interactive video system created by renowned experts. 

You will optimize the potential of data storage in the best digital university in the world according to Forbes"

 

You will be able to access exclusive content on the virtual campus 24 hours a day, with no geographical or time restrictions"

This syllabus has been designed by a team of experts in the area of Artificial Intelligence, placing special emphasis on knowledge discovery processes and machine learning. Thanks to this, the students will delve into the development of algorithms and models that allow machines to learn patterns and perform tasks without having been explicitly programmed for that task. In addition, TECH uses the effective Relearningmethodology, in which it is a pioneer. In this way, professionals will integrate strong knowledge into model evaluation in a progressive and effective manner. 

You will delve into the formulation of genetic algorithms through 18 months of the best digital teaching to boost your professional development" 

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 Data 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 their 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 Indices
2.7.3. Data Mining

2.8. Data Warehouse (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/Safety

2.10. Regulatory Aspects 

2.10.1. Data Protection Law
2.10.2. Good Practices
2.10.3. Other Normative Aspects

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. Mathematical Analysis Criteria for 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 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. Algorithm ID 
7.3.2. Algorithm C 
7.3.3. Overtraining and Pruning 
7.3.4. Analysis of Results 

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. Cloak 
8.3.3. Output layer 

8.4. Union of Layers 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. Training of Deep Neural Networks 

9.1. Gradient problems 

9.1.1. Techniques of optimization of gradient 
9.1.2. Stochastic gradients 
9.1.3. Techniques of initialization of weights 

9.2. Reuse of pre-formed 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 drop optimizers 
9.3.2. Optimizers Adam and RMSprop 
9.3.3. Optimizers at the moment 

9.4. Programming of the learning rate 

9.4.1. Control of machine learning rate 
9.4.2. Learning cycles 
9.4.3. Softening terms 

9.5. Overadjustment 

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 tests 

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. Generation of synthetic data 
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 graphics 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 graphics 

10.3. Customization of training models and 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 graphics 

10.4.1. Functions with TensorFlow 
10.4.2. Use of graphics for model training 
10.4.3. Graphics 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 API tfdata 

10.6.1. Using the tfdataAPI for data processing 
10.6.2. Construction of data streams with tfdata 
10.6.3. Using the tfdataAPI 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. Layers of preprocessing of Keras 

10.8.1. Using the Keras Preprocessing API 
10.8.2. Preprocessing pipelined construction with Keras 
10.8.3. Using Keras Preprocessing API for Model Training 

10.9. The TensorFlow Datasets project 

10.9.1. Using TensorFlow Datasets  for data loading 
10.9.2. Preprocessing Data with TensorFlow Datasets  
10.9.3. Using TTensorFlow Datasets  for model training 

10.10. Building a Deep Learning App with TensorFlow 

10.10.1. Practical Application 
10.10.2. Building a Deep Learning App with TensorFlow 
10.10.3. Model training with TensorFlow 
10.10.4. Using the application to predict 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 the computational vision 
11.1.3. Image processing models 

11.2. Convolutional layers 

11.2.1. Reuse of weights in convolution 
11.2.2. Convolution D 
11.2.3. Activation Functions 

11.3. Layers of grouping and implementation of layers of grouping 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. Architecture ResNet 

11.5. Implementing a CNN ResNet- using Keras 

11.5.1. Initialization of weights 
11.5.2. Definition of the input layer 
11.5.3. Definition of the output 

11.6. Use of pre-trained Keras models 

11.6.1. Characteristics of the pre-trained models 
11.6.2. Uses of the pre-trained models 
11.6.3. Advantages of pre-trained models 

11.7. Pre-training models for transfer learning 

11.7.1. Learning by Transfer 
11.7.2. Learning process by transfer 
11.7.3. The benefits of transfer learning 

11.8. Deep Computer Vision Classification and Localization 

11.8.1. Image Classification 
11.8.2. Location of objects in images 
11.8.3. Object Detection 

11.9. Object detection and object tracking 

11.9.1. Methods of detection of objects 
11.9.2. Algorithms for tracking objects 
11.9.3. Tracking and tracing techniques 

11.10. Semantic Segmentation 

11.10.1. Deep learning for semantic segmentation 
11.10.2. Edge Detection 
11.10.3. Segmentation methods based on rules 

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

12.1. Text generation using RNN 

12.1.1. RNN training for text generation 
12.1.2. Natural language generation with RNN 
12.1.3. Text generation applications with RNN 

12.2. Creating the training data set 

12.2.1. Preparing data for NRN training 
12.2.2. Storage of training data set 
12.2.3. Cleaning and transformation of data 
12.2.4. Sentiment Analysis 

12.3. Rating of reviews with RNN 

12.3.1. Detection of topics 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 machine translation accuracy with RNN 

12.5. Care mechanisms 

12.5.1. Application of care mechanisms in NRN 
12.5.2. Use of care mechanisms to improve model accuracy 
12.5.3. Advantages of attention mechanisms in neural networks 

12.6. Transformers Models 

12.6.1. Using Transformersmodels for natural language processing 
12.6.2. Application of Transformersmodels for vision 
12.6.3. Advantages of the Transformers models 

12.7. Transformers for vision 

12.7.1. Use of Transformers models for vision 
12.7.2. Preprocessing of the image data 
12.7.3. Training a Transformers model for vision 

12.8. Hugging Face’s TransformersBookstore 

12.8.1. Using the Hugging Face TransformersLibrary 
12.8.2. Hugging Face TransformersLibrary App 
12.8.3. Advantages of Hugging Face’s Transformerslibrary 

12.9. Other bookstores of Transformers. Comparison 

12.9.1. Comparison between different Transformerslibraries 
12.9.2. Use of other Transformerslibraries 
12.9.3. Advantages of the other Transformerslibraries 

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

12.10.1. Development of a natural language processing application with RNN and care 
12.10.2. Use of RNN, attention mechanisms and Transformersmodels in the application 
12.10.3. Evaluation of the practical implementation 

Module 13. Autoencoders, GANs, and Diffusion Models 

13.1. Efficient data representations 

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

13.2. Realization of PCA with an incomplete linear automatic encoder 

13.2.1. Training process 
13.2.2. Python implementation 
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. Autocodificadores convolucionales 

13.4.1. Design of convolutional models 
13.4.2. Training of convolutional models 
13.4.3. Results Evaluation 

13.5. Noise elimination from automatic encoders 

13.5.1. Filter application 
13.5.2. Design of coding models 
13.5.3. Use of regularization techniques 

13.6. Dispersed automatic encoders 

13.6.1. Increase the efficiency of coding 
13.6.2. Minimizing the number of parameters 
13.6.3. Use of regularization techniques 

13.7. Automatic variational 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. Training of Deep Neural Networks 

13.9. Generative adversarial networks and dissemination models 

13.9.1. Generation of content 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 Application 
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 (AI) in financial services. Opportunities and challenges
15.1.2. Case Uses
15.1.3. Potential Risks Related to the Use of AI 
15.1.4. Potential Future Developments/Uses of AI 

15.2. Implications of Artificial Intelligence in the Healthcare Service

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

15.3. Risks Related to the Use of AI in the Health Service 

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

15.4. Retail

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

15.5. Industry 

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

15.6. Potential risks related to the use of AI in industry 

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

15.7. Public Administration.

15.7.1. AI implications for public administration. Opportunities and Challenges 
15.7.2. Case Uses
15.7.3. Potential Risks Related to the Use of AI
15.7.4. Potential Future Developments/Uses of AI

15.8. Educational

15.8.1. AI implications for education. Opportunities and Challenges 
15.8.2. Case Uses
15.8.3. Potential Risks Related to the Use of AI
15.8.4. Potential Future Developments/Uses of AI 

15.9. Forestry and Agriculture

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

15.10. Human Resources

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

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