Optimize Advertising Campaigns in the best digital university in the world according to Forbes"

To differentiate themselves from their competitors, companies engaged in Digital Marketing seek to implement the most advanced Machine Learning tools to improve their websites. In this sense, companies make it a top priority to personalize user experiences in order to establish relationships based on trust and loyalty. One of the most effective tools in this regard is Chatbots or Virtual Assistants. These intelligent systems provide personalized attention to customers throughout the day.  They help to resolve consumer queries globally and to maintain a constant online presence.

In view of this, TECH launches an innovative program that will offer experts the most effective AI strategies in online advertising. Designed by experts in the field, the syllabus will delve into Predictive Analytics and Big Data. In line with this, the syllabus will emphasize Email Marketing for campaign personalization. Likewise, the didactic materials will delve into the application of Machine Learning in market research and for the visualization of significant data. On the other hand, the program will address specific techniques for Leads generation with AI and the integration of Autonomous Systems in competitive analysis.

In addition, the academic itinerary is designed with a theoretical-practical perspective and has numerous complementary didactic materials to strengthen learning in a dynamic way (including interactive summaries, detailed videos or case studies). Students will be able to access the Virtual Campus at any time of the day. The only requirement is that students have a digital device capable of accessing the Internet. This is a university program that does not require attendance at centers and does not have pre-set class schedules. Professionals will thus have greater freedom to self-manage their access time and reconcile their daily activities with top-quality teaching.

You will be able to eliminate the noise of automatic coders to improve users' digital experiences”

This Professional master’s degree in Artificial Intelligence in Marketing and Communication contains the most complete and up-to-date educational program on the market. Its most notable features are:

• The development of case studies presented by experts inArtificial Intelligence in Marketing and Communication
• The graphic, schematic and eminently practical content of the system provides complete and practical information on those 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 acquire the most effective skills to incorporate Machine Learning resources into sales management”

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

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

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

You will optimize the effectiveness of your marketing strategies by fostering a closer and more personalized relationship with customers"

Take advantage of all the benefits of the Relearning methodology: it will allow you to organize your time and study pace, adapting to your schedule"

This Professional master’s degree will stand out for its comprehensive approach as well as for its top-quality syllabus. Composed of 20 modules, the syllabus will delve into Content Generation through AI. Likewise, the university program will analyze the Automation and Optimization of Processes with Machine Learning, which will allow students to enrich their professional practice with the most advanced strategies. On the other hand, the didactic contents will pay special attention to future trends, so that graduates can benefit from them and overcome any challenge they may face during their respective activities.

This online methodology allows you, through case studies, to practice in simulated environments to extract valuable lessons"

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-based 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. Creation of 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. 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 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. 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. 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. Setting 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 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. 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 Graphics 

10.4.1. Functions with TensorFlow 
10.4.2. Use of Graphs for Model Training 
10.4.3. Graphics Optimization with TensorFlowOperations 

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 tfdata API for Model Training 

10.7. The TFRecord Format 

10.7.1. Using the TFRecord API for Data Serialization 
10.7.2. Loading TFRecord Files with TensorFlow 
10.7.3. Using TFRecord Files for Training Models 

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 Datasetsfor data loading 
10.9.2. Preprocessing Data 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. Architecture ResNet 

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 
11.10.2. Edge Detection 
11.10.3. Rule-based Segmentation Methods

Module 12. Natural Language Processing (NLP) with Natural Recurrent Networks (NNN) 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. Transformers Models 

12.6.1. Using TransformersModels 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’sTransformer Library 

12.8.1. Using the Hugging Face's Transformers Library 
12.8.2. Hugging Face´s Transformers Library App 
12.8.3.  Advantages of Hugging Face´s Transformers Library 

12.9. Other Transformers Libraries. Comparison 

12.9.1. Comparison between Different TransformersLibraries 
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 Applications 

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. Automatic Encoder Denoising 

13.5.1. Application of Filters 
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 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 

Module 16. Artificial Intelligence in Digital Marketing Strategies  

16.1. Digital Marketing Transformation with AI 

16.1.1. Introduction to Digital Transformation 
16.1.2. Impact on Content Strategy 
16.1.3. Automation of Marketing Processes 
16.1.4. Customer Experience Development 

16.2. AI Tools for SEO and SEM 

16.2.1. Keyword Optimization with AI 
16.2.2. Competition Analysis 
16.2.3. Search Trend Prediction 
16.2.4. Intelligent Audience Segmentation 

16.3. Application of AI in Social Media 

16.3.1. Sentiment Analysis 
16.3.2. Social Trend Detection 
16.3.3. Publication Automation 
16.3.4. Automated Content Generation 

16.4. AI Tools for Customer Communication 

16.4.1. Personalized Chatbots 
16.4.2. Automated E-mail Response Systems 
16.4.3. Real-Time Response Optimization 
16.4.4. Customer Feedback Analysis 

16.5. Personalization of the User Experience of AI-enabled Tools and Websites 

16.5.1. Personalized Recommendations 
16.5.2. User Interface Adaptation 
16.5.3. Dynamic Audience Segmentation 
16.5.4. Intelligent A/B Testing 

16.6. Chatbots and Virtual Assistants in Digital Marketing 

16.6.1. Proactive Interaction 
16.6.2. Multichannel Integration 
16.6.3. Contextual Responses 
16.6.4. Conversation Analytics 

16.7. Programmatic Advertising with AI 

16.7.1. Advanced Targeting 
16.7.2. Real-Time Optimization 
16.7.3. Automatic Bidding 
16.7.4. Results Analysis 

16.8. Predictive Analytics and Big Data in Digital Marketing 

16.8.1. Prediction of Market Trends 
16.8.2. Advanced Attribution Models 
16.8.3. Predictive Audience Segmentation 
16.8.4. Sentiment Analysis in Big Data 

16.9. AI and Email Marketing for Personalization and Automation in Campaigns 

16.9.1. Dynamic List Segmentation 
16.9.2. Dynamic Content in Emails 
16.9.3. Workflow Automation 
16.9.4. Open Rate Optimization 

16.10. Future Trends in AI for Digital Marketing 

16.10.1. Advanced Conversational AI 
16.10.2. Augmented Reality Integration 
16.10.3. Emphasis on AI Ethics 
16.10.4. AI in Content Creation

Module 17. Content Generation with AI

17.1. Prompt Engineering in ChatGPT  

17.1.1. Improving the Quality of the Generated Content 
17.1.2. Strategies to Optimize Model Performance 
17.1.3. Designing Effective Prompts 

17.2. AI Image Generation Tools 

17.2.1. Object Recognition and Generation 
17.2.2. Applying Custom Styles and Filters to Images 
17.2.3. Methods to Improve the Visual Quality of Images 

17.3. Creating Videos with AI 

17.3.1. Tools to Automate Video Editing 
17.3.2. Voice Synthesis and Automatic Dubbing 
17.3.3. Techniques for Object Tracking and Animation 

17.4. Text Generation with AI for Blogging and Social Networking 

17.4.1. Strategies for Improving SEO Positioning in Generated Content 
17.4.2. Using AI to Predict and Generate Content Trends 
17.4.3. Creation of Attractive Headlines 

17.5. Personalizing Content with AI for Different Audiences 

17.5.1. Identification and Analysis of Audience Profiles 
17.5.2. Dynamic Adaptation of Content according to User Profiles 
17.5.3. Predictive Audience Segmentation 

17.6. Ethical Considerations for the Responsible Use of AI in Content Generation 

17.6.1. Transparency in Content Generation 
17.6.2. Prevention of Bias and Discrimination in Content Generation 
17.6.3. Control and Human Supervision in Generative Processes 

17.7. Analysis of Successful Cases in Content Generation with AI 

17.7.1. Identification of Key Strategies in Successful Cases 
17.7.2. Adaptation to Different Sectors 
17.7.3. Importance of Collaboration between AI Specialists and Industry Practitioners 

17.8. Integration of AI-generated Content in Digital Marketing Strategies 

17.8.1. Optimization of Advertising Campaigns with Content Generation 
17.8.2. User Experience Personalization 
17.8.3. Automation of Marketing Processes 

17.9. Future Trends in the Generation of Content with AI 

17.9.1. Advanced and Seamless Integration of Text, Image and Audio 
17.9.2. Hyper-personalized Content Generation 
17.9.3. Improved AI Development in Emotion Detection 

17.10. Evaluating and Measuring the Impact of AI-generated Content 

17.10.1. Appropriate Metrics to Evaluate the Performance of Generated Content  
17.10.2. Measurement of Audience Engagement 
17.10.3. Continuous Improvement of Content through Analysis

Module 18. Automation and Optimization of Marketing Processes with AI 

18.1. Marketing Automation with AI 

18.1.1. AI-based Audience Segmentation 
18.1.2. Workflow Automation 
18.1.3. Continuous Optimization of Online Campaigns 

18.2. Integration of Data and Platforms in Automated Marketing Strategies 

18.2.1. Analysis and Unification of Multichannel Data 
18.2.2. Interconnection between Different Marketing Platforms 
18.2.3. Real-time Data Updating 

}18.3. Optimization of Advertising Campaigns with AI 

18.3.1. Predictive Analysis of Ad Performance 
18.3.2. Automatic Personalization of the Advertisement according to the Target Audience 
18.3.3. Automatic Budget Adjustment according to Results 

18.4. Audience Personalization with AI 

18.4.1. Content Segmentation and Personalization 
18.4.2. Personalized Content Recommendations 
18.4.3. Automatic Identification of Homogeneous Audiences or Groups 

18.5. Automation of Responses to Customers through AI  

18.5.1. Chatbots and Machine Learning 
18.5.2. Automatic Generation of Responses 
18.5.3. Automatic Problem Solving 

18.6. AI in Email Marketing for Automation and Personalization 

18.6.1. Automation of Email Sequences 
18.6.2. Dynamic Personalization of Content according to Preferences 
18.6.3. Intelligent Segmentation of Mailing Lists 

18.7. Sentiment Analysis with AI in Social Media and Customer Feedback 

18.7.1. Automatic Sentiment Monitoring in Comments 
18.7.2. Personalized Responses to Emotions 
18.7.3. Predictive Reputation Analytics 

18.8. Price and Promotion Optimization with AI 

18.8.1. Automatic Price Adjustment based on Predictive Analytics 
18.8.2. Automatic Generation of Offers Adapted to User Behavior 
18.8.3. Real-time Competitive and Price Analysis 

18.9. Integration of AI into Existing Marketing Tools 

18.9.1. Integration of AI Capabilities with Existing Marketing Platforms 
18.9.2. Optimization of Existing Functionalities 
18.9.3. Integration with CRM Systems 

18.10. Trends and Future of Automation with AI in Marketing 

18.10.1. AI to Improve User Experience 
18.10.2. Predictive Approach to Marketing Decisions 
18.10.3. Conversational Advertising

Module 19. Communication and Marketing Data Analysis for Decision Making  

19.1. Specific Technologies and Tools for Communication and Marketing Data Analysis 

19.1.1. Tools for Analyzing Conversations and Trends in Social Media 
19.1.2. Systems to Identify and Evaluate Emotions in Communications 
19.1.3. Use of Big Data to Analyze Communications 

19.2. Applications of AI in the Analysis of Large Volumes of Marketing Data 

19.2.1. Automatic Processing of Massive Data 
19.2.2. Identification of Behavioral Patterns 
19.2.3. Optimization of Algorithms for Data Analysis 

19.3. Tools for Data Visualization and Reporting of Campaigns and Communications with AI

19.3.1. Creation of Interactive Dashboards 
19.3.2. Automatic Report Generation 
19.3.3. Predictive Visualization of Campaign Results 

19.4. Application of AI in Market Research 

19.4.1. Automatic Processing of Survey Data 
19.4.2. Automatic Identification of Audience Segments 
19.4.3. Prediction of Market Trends 

19.5. Predictive Analytics in Marketing for Decision Making 

19.5.1. Predictive Models of Consumer Behavior 
19.5.2. Campaign Performance Forecasting
19.5.3. Automatic Adjustment of Strategic Optimization 

19.6. Market Segmentation with AI 

19.6.1. Automated Analysis of Demographic Data 
19.6.2. Identification of Interest Groups 
19.6.3. Dynamic Personalization of Offers 

19.7. Marketing Strategy Optimization with AI 

19.7.1. Use of AI to Measure Channel Effectiveness 
19.7.2. Strategic Automatic Adjustment to Maximize Results 
19.7.3. Simulation of Strategic Scenarios 

19.8. AI in Marketing ROI Measurement 

19.8.1. Conversion Attribution Models 
19.8.2. ROI Analysis using AI 
19.8.3. Customer Lifetime Value Estimation 

19.9. Success Stories in Data Analytics with AI 

19.9.1. Demonstration through Case Studies where AI has Improved Results 
19.9.2. Cost and Resource Optimization 
19.9.3. Competitive Advantages and Innovation 

19.10. Challenges and Ethical Considerations in AI Data Analysis 

19.10.1. Biases in Data and Results 
19.10.2. Ethical Considerations in Handling and Analyzing Sensitive Data 
19.10.3. Challenges and Solutions for Making AI Models Transparent

Module 20. Sales and Leads Generation with Artificial Intelligence

20.1. Application of AI in the Sales Process 

20.1.1. Automation of Sales Tasks 
20.1.2. Predictive Sales Cycle Analysis 
20.1.3. Optimization of Pricing Strategies 

20.2. Techniques and Tools for Lead Generation with AI 

20.2.1. Automated Lead Identification 
20.2.2. User Behavior Analysis 
20.2.3. Personalization of Content for Recruitment 

20.3. Leads Scoring with AI 

20.3.1. Automated Evaluation of Leads Qualification  
20.3.2. Lead Analysis Based on Interactions 
20.3.3. Leads Scoring Model Optimization 

20.4. AI in Customer Relationship Management 

20.4.1. Automated Tracking to Improve Customer Relationships
20.4.2. Personalized Recommendations for Customers 
20.4.3. Automation of Personalized Communications 

20.5. Implementation and Success Cases of Virtual Assistants in Sales 

20.5.1. Virtual Assistants for Sales Support 
20.5.2. Improving Customer Experience 
20.5.3. Optimizing Conversions and Closing Sales 

20.6. Predicting Customer Needs with AI 

20.6.1. Analysis of Buying Behavior 
20.6.2. Dynamic Offer Segmentation 
20.6.3. Personalized Recommendation Systems 

20.7. Personalization of the Sales Offer with AI 

20.7.1. Dynamic Adaptation of Sales Proposals 
20.7.2. Behavior-based Exclusive Offers 
20.7.3. Creation of Customized Packs 

20.8. Competitive Analysis with AI 

20.8.1. Automated Competitor Monitoring 
20.8.2. Automated Comparative Price Analysis 
20.8.3. Predictive Competitive Surveillance 

20.9. Integration of AI in Sales Tools 

20.9.1. Compatibility with CRM Systems 
20.9.2. Empowerment of Sales Tools 
20.9.3. Predictive Analytics in Sales Platforms 

20.10. Innovations and Predictions in the Sales Environment 

20.10.1. Augmented Reality in Shopping Experience 
20.10.2. Advanced Automation in Sales 
20.10.3. Emotional Intelligence in Sales Interactions

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