A comprehensive and 100% online program, exclusive to TECH, with an international perspective supported by our membership with The Society for the Study of Artificial Intelligence and Simulation of Behaviour”

Artificial Intelligence has revolutionized the field of Marketing and Communication, enabling process automation, large-scale data analysis, and real-time personalization of strategies. Companies across all industries are now seeking professionals capable of integrating machine learning into their business models to optimize advertising campaigns, enhance customer experience, and strengthen strategic decision-making. To seize these opportunities, professionals must acquire a competitive edge that sets them apart from other candidates. 

For this reason, TECH has launched an innovative Master's Degree in Artificial Intelligence in Marketing and Communication. Designed by leading experts in the sector, this academic pathway explores a wide range of topics, from advanced big data techniques and deep neural network training to the implementation of automated chatbots. Furthermore, the syllabus provides strategies to optimize market segmentation, automate client interaction, and analyze consumer behavior in real time. Thanks to this comprehensive approach, students will develop the skills to design personalized campaigns based on intelligent systems that maximize communicative impact and return on investment. 

In this line, TECH offers a 100% online educational setting, tailored to the needs of busy professionals looking to advance their careers. In addition, through its Relearning methodology, the program ensures an efficient and natural learning process. This way, professionals will not need to dedicate long hours to study or memorization. They will only require a device with internet access to enter the Virtual Campus. 

Thanks to TECH's membership with the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB), students will have access to digital publications such as AISB and Discussions, as well as a weekly newsletter with news and job offers. Additionally, they will enjoy discounted rates for AISB and ECAI conferences, receive travel support, and training to create local groups.

You will design high-performance advertising campaigns using cutting-edge predictive algorithms” 

This Master's Degree in Artificial Intelligence in Marketing and Communication 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 in Marketing and Communication
• The graphic, schematic, and practical contents with which they are created, provide scientific and practical information on the disciplines that are essential for professional practice
• Practical exercises where 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 access a learning system based on repetition, with a natural and progressive teaching throughout the entire syllabus” 

It includes in its teaching staff professionals belonging to the field of Artificial Intelligence in Marketing and Communication, who bring to this program their work experience, as well as recognized specialists from leading companies 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 an immersive learning experience designed to prepare for real-life situations.

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

You will achieve your objectives thanks to TECH’s didactic tools, including explanatory videos and interactive summaries”

You will develop automated strategies that maximize both conversion and impact on segmented audiences”

The contents of this program have been designed by experts in Artificial Intelligence applied to Marketing and Communication. Throughout the curriculum, graduates will explore everything from the foundations of predictive analytics to its implementation in business strategies. The syllabus will also provide students with innovative machine learning techniques to enhance message personalization and optimize the customer experience across multiple digital channels. In addition, the degree will delve into the use of intelligent chatbots and automation systems to improve interaction with the target audience.

You will implement chatbots to respond to customer inquiries immediately and in real time, with a personalized approach” 

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 Statistical Data

2.2.1. By 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. By Form

2.2.2.1. Numerical
2.2.2.2. Text
2.2.2.3. Logical

2.2.3. By Source

2.2.3.1. Primary
2.2.3.2. Secondary

2.3. Data Lifecycle

2.3.1. Lifecycle Stages
2.3.2. Lifecycle Milestones
2.3.3. FAIR Principles

2.4. Initial Stages of the Cycle

2.4.1. Goal Definition
2.4.2. Determination of Required Resources
2.4.3. Gantt Chart
2.4.4. Data Structure

2.5. Data Collection

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

2.6. Data Cleaning

2.6.1. Data Cleaning Phases
2.6.2. Data Quality
2.6.3. Data Manipulation (using 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

2.8.1. Components of a Data Warehouse
2.8.2. Design
2.8.3. Aspects to Consider

2.9. Data Availability

2.9.1. Access
2.9.2. Usefulness
2.9.3. Security

2.10. Regulatory Aspects

2.10.1. Data Protection Law
2.10.2. Best Practices
2.10.3. Other Regulatory 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. Greedy Strategy Elements
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. Minimum Spanning 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 Algorithm
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. Creation of 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 Matrices
7.4.2. Numerical Evaluation Matrices
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. Operations

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

9.3. Optimizers

9.3.1. Stochastic Gradient Descent Optimizers
9.3.2. Adam and RMSprop Optimizers
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. Transfer Learning 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. Transfer Learning 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. Using the TensorFlow Library
10.1.2. Model Education with TensorFlow
10.1.3. Operations with Graphs in TensorFlow

10.2. TensorFlow and NumPy

10.2.1. NumPy Computational 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 Functions and Graphs

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

10.5. Data Loading and Pre-Processing with TensorFlow

10.5.1. Loading Datasets with TensorFlow
10.5.2. Data Pre-Processing with TensorFlow
10.5.3. Using TensorFlow Tools for Data Manipulation

10.6. The tfdata API

10.6.1. Using the tfdata API 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 Pre-Processing Layers

10.8.1. Using the Keras Pre-Processing API
10.8.2. Construction of Pre-Processing Pipelined with Keras
10.8.3. Using the Keras Pre-Processing 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 Application
10.10.2. Building a Deep Learning App with TensorFlow
10.10.3. Training a Model with TensorFlow
10.10.4. Using 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. Learning by Transfer
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 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 Pre-Processing
12.7.3. Training a Transformers Model for Vision

12.8. Hugging Face Transformer Library

12.8.1. Using the Hugging Face Transformers Library
12.8.2. Application of the Hugging Face Transformers Library
12.8.3. Advantages of the Hugging Face 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 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. Use Cases
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 Healthcare Service

15.2.1. Implications of AI in the Healthcare Sector. Opportunities and Challenges
15.2.2. Use Cases

15.3. Risks Related to the Use of AI in Healthcare 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. Use Cases
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. Use Cases

15.6. Potential Risks Related to the Use of AI in Industry

15.6.1. Use Cases
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. Use Cases
15.7.3. Potential Risks Related to the Use of AI
15.7.4. Potential Future Developments/Uses of AI

15.8. Education

15.8.1. AI Implications for Education. Opportunities and Challenges
15.8.2. Use Cases
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. Use Cases
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 in Human Resources. Opportunities and Challenges
15.10.2. Use Cases
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 and ChatGPT

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

16.2. AI Tools for SEO and SEM: Keyword Insights and DiiB

16.2.1. Keyword Optimization with AI
16.2.2. Competitive Analysis
16.2.3. Search Trend Forecast
16.2.4. Intelligent Audience Segmentation

16.3. IA Application in Social Media

16.3.1. Sentiment Analysis with MonkeyLearn
16.3.2. Social Trend Detection
16.3.3. Publication Automation with Metricool
16.3.4. Automated Content Generation with Predis

16.4. AI Tools for Customer Communication

16.4.1. Custom Chatbots Using Dialogflow
16.4.2. Automated Email Response Systems Using Mailchimp
16.4.3. Real-Time Response Optimization Using Freshchat
16.4.4. Customer Feedback Analysis Using SurveyMonkey

16.5. User Experience Personalization with AI

16.5.1. Personalized Recommendations
16.5.2. User Interface Adaptation
16.5.3. Dynamic Audience Segmentation
16.5.4. Intelligent A/B Testing with VWO (Visual Website Optimizer)

16.6. Chatbots and Virtual Assistants in Marketing Digital

16.6.1. Proactive Interaction with Customers.ai
16.6.2. Multichannel Integration Using Tars
16.6.3. Contextual Responses with Chatfuel
16.6.4. Conversation Analytics Using Botpress

16.7. Programmatic Advertising with AI

16.7.1. Advanced Segmentation with Adroll
16.7.2. Real-Time Optimization Using WordStream
16.7.3. Automatic Bidding Using BidIQ
16.7.4. Result 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 Campaign Customization and Automation

16.9.1. Dynamic List Segmentation
16.9.2. Dynamic Content in Emails
16.9.3. Workflow Automation with Brevo
16.9.4. Optimizing Open Rate with Benchmark Email

16.10. Future Trends in AI for Digital Marketing

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

Module 17. Content Generation with Artificial Intelligence

17.1. Prompt Engineering in ChatGPT

17.1.1. Quality Improvement of the Generated Content
17.1.2. Model Performance Optimization Strategies
17.1.3. Effective Prompt Design

17.2. AI Image Generation Tools through ChatGPT

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. Video Creation 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. AI Text Generation for Blogging and Social Media Creation through ChatGPT

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. Creating Attractive Headlines

17.5. Personalization of AI Content to Different Audiences Using Optimizely

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. Preventing 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 Content Generation with AI

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

17.10. Evaluation and Measurement of 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 Analytics

Module 18. Automation and Optimization of Marketing Processes with Artificial Intelligence

18.1. Marketing Automation with AI Using Hubspot

18.1.1. Audience Segmentation Based on AI
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 through Google Ads

18.3.1. Predictive Analysis of Advertising Performance
18.3.2. Automatic Advertisement Personalization According to Target Audience
18.3.3. Automatic Budget Adjustment Based on 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 Audiences or Homogeneous Groups

18.5. Automation of Responses to Customers through AI

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

18.6. AI in Email Marketing for Automation and Customization

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

18.7. Social Media Sentiment Analysis with AI and Customer Feedback through Lexalytics

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

18.8. Price and Promotions Optimization with AI through Vendavo

18.8.1. Automatic Price Adjustment Based on Predictive Analysis
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 Marketing Automation with AI

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

Module 19. Analysis of Communication and Marketing Data for Decision Making

19.1. Specific Technologies and Tools for Communication and Marketing Data Analysis using Google Analytics 4

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. AI Applications in Marketing Big Data Analytics such as Google BigQuery

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 through Quid

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

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 using Meta

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. Scenario Simulation

19.8. AI in Marketing ROI Measurement with GA4

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 by Practical Cases in which 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 Lead Generation with Artificial Intelligence

20.1. Application of AI in the Sales Process through Salesforce

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

20.2. Lead Generation Techniques and Tools with AI through Hubspot

20.2.1. Automated Prospect Identification
20.2.2. User Behavior Analysis
20.2.3. Personalization of Content for Engagement

20.3. Lead Scoring with AI using Hubspot

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

20.4. AI in Customer Relationship Management

20.4.1. Automated Tracking to Improve Customer Relationships
20.4.2. Personalized Customer Recommendations
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. Customer Experience Improvement
20.5.3. Conversion Rate Optimization and Sales Closing

20.6. Predicting Customer Needs with AI

20.6.1. Purchase Behavior Analysis
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 Analysis 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

You will master sophisticated predictive analysis and data mining techniques to deepen understanding of consumer behavior”