Master the future of Digital Marketing and transformation with this 100% online program exclusively offered by TECH”

By better understanding user preferences and behaviors, AI optimizes segmentation, enhances customer experience, and boosts campaign effectiveness. However, integrating AI into Digital Marketing also presents various challenges.

In recent years, Artificial Intelligence has made significant advances that have transformed Digital Marketing. Machine learning algorithms and natural language processing now enable real-time data analysis, campaign optimization, and consumer behavior prediction. Digital marketing professionals have adopted these technologies to create personalized experiences, automate repetitive tasks, and make more informed decisions.

This evolution has created a constant need for marketing experts to update their skills with new tools and approaches, as AI and related technologies evolve rapidly. Today, professionals must not only understand traditional marketing strategies but also how to effectively integrate AI into their campaigns. This is the foundation of our Artificial Intelligence in Digital Marketing program, where students will explore content personalization and recommendations using tools like Adobe Sensei, audience segmentation, market analysis, trend prediction, and consumer behavior. The program also covers campaign optimization and AI applications in personalized advertising, advanced ad targeting, budget optimization, and predictive analytics for campaign performance.

This 100% online program allows graduates to learn comfortably from anywhere, at any time, with just an Internet-connected device. Based on the Relearning methodology, which reinforces key concepts to ensure optimal retention, it offers a flexible format tailored to today’s demands. It prepares marketing professionals to excel in a fast-growing, high-demand industry.

You will acquire a deep mastery of AI and apply advanced strategies to optimize campaigns and personalize unique experiences”

This Master's Degree in Artificial Intelligence in Digital Marketing contains the most complete and up-to-date educational program on the market. Its most notable features are:

• The development of practical case studies presented by experts in Artificial Intelligence in Digital Marketing
• 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 the self-assessment process can be carried out to improve learning
• Special emphasis on innovative methodologies in Artificial Intelligence in Digital Marketing
• 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

Become a global benchmark by integrating Artificial Intelligence strategies in Digital Marketing in the most influential organizations in the world”

It includes in its teaching staff professionals belonging to the field of Artificial Intelligence in Digital Marketing, who bring their work experience to this program, as well as recognized specialists from prestigious companies and 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.

With a 100% program you can equip yourself with the tools and knowledge necessary to transform global challenges into competitive advantages"

Access comprehensive and up-to-date syllabus designed to master AI in Digital Marketing and stand out in a competitive marketplace"

The syllabus combines advanced theory with practical applications of AI in Marketing. Throughout the program, students will master advanced data analysis tools, develop personalized strategies using artificial intelligence, and optimize campaigns in real time. With key modules on audience segmentation, automation, predictive analytics, and insight generation, students will be prepared to meet market challenges. Additionally, the program addresses ethics and data privacy, ensuring a comprehensive education adaptable to current digital demands.

This program connects you with exclusive industry opportunities through practical projects and real-world case studies”

Module 1. Fundamentals of Artificial Intelligence

1.1. History of Artificial Intelligence

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

1.2. Artificial Intelligence in Games

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

1.3. Neural Networks

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

1.4. Genetic Algorithms

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

1.5. Thesauri, Vocabularies, Taxonomies

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

1.6. Semantic Web

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

1.7. Expert Systems and DSS

1.7.1. Expert Systems
1.7.2. Decision Support Systems

1.8. Chatbots and Virtual Assistants

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

1.9. AI Implementation Strategy
1.10. Future of Artificial Intelligence

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

Module 2. Data Types and Life Cycle

2.1. Statistics

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

2.2. Types of Data Statistics

2.2.1. According to Type

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

2.2.2. According to Its Shape

2.2.2.1. Numeric
2.2.2.2. Text
2.2.2.3. Logical

2.2.3. According to Its Source

2.2.3.1. Primary
2.2.3.2. Secondary

2.3. Life Cycle of Data

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

2.4. Initial Stages of the Cycle

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

2.5. Data Collection

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

2.6. Data Cleaning

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

2.7. Data Analysis, Interpretation and Evaluation of Results

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

2.8. Datawarehouse

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

2.9. Data Availability

2.9.1. Access
2.9.2. Uses
2.9.3. Security

2.10. Regulatory Aspects

2.10.1. Data Protection Law
2.10.2. Good 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. Sorting by Merge (Merge_Sort)
5.3.6. Sorting Quickly (Quick_Sort)

5.4. Algorithms with Trees

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

5.5. Algorithms Using Heaps

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

5.6. Graph Algorithms

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

5.7. Greedy Algorithms

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

5.8. Minimal Path Finding

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

5.9. Greedy Algorithms on Graphs

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

5.10. Backtracking

5.10.1. Backtracking
5.10.2. Alternative Techniques

Module 6. Intelligent Systems

6.1. Agent Theory

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

6.2. Agent Architectures

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

6.3. Information and Knowledge

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

6.4. Knowledge Representation

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

6.5. Ontologies

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

6.6. Ontology Languages and Ontology Creation Software

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

6.7. Semantic Web

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

6.8. Other Knowledge Representation Models

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

6.9. Knowledge Representation Assessment and Integration

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

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

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

Module 7. Machine Learning and Data Mining

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

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

7.2. Data Exploration and Pre-Processing

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

7.3. Decision Trees

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

7.4. Evaluation of Classifiers

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

10.2. TensorFlow and NumPy

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

10.3. Model Customization and Training Algorithms

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

10.4. TensorFlow Features and Graphs

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

10.5. Loading and Preprocessing Data with TensorFlow

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

10.6. The tf.data API

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

10.7. The TFRecord Format

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

10.8. Keras Pre-Processing Layers

10.8.1. Using the Keras Pre-Processing API
10.8.2. Pre-Processing Pipelined Construction 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 Pre-Processing 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. 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. 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 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’s Transformers Library

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

12.9. Other Transformers Libraries. Comparison

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

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

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

Module 13. Autoencoders, GANs and Diffusion Models

13.1. Representation of Efficient Data

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

13.2. PCA Realization with an Incomplete Linear Automatic Encoder

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

13.3. Stacked Automatic Encoders

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

13.4. Convolutional Autoencoders

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

13.5. Noise Suppression of Automatic Encoders

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

13.6. Sparse Automatic Encoders

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

13.7. Variational Automatic Encoders

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

13.8. Generation of Fashion MNIST Images

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

13.9. Generative Adversarial Networks and Diffusion Models

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

13.10. Implementation of the Models

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

Module 14. Bio-Inspired Computing

14.1. Introduction to Bio-Inspired Computing

14.1.1. Introduction to Bio-Inspired Computing

14.2. Social Adaptation Algorithms

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

14.3. Genetic Algorithms

14.3.1. General Structure
14.3.2. Implementations of the Major Operators

14.4. Space Exploration-Exploitation Strategies for Genetic Algorithms

14.4.1. CHC Algorithm
14.4.2. Multimodal Problems

14.5. Evolutionary Computing Models (I)

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

14.6. Evolutionary Computation Models (II)

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

14.7. Evolutionary Programming Applied to Learning Problems

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

14.8. Multi-Objective Problems

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

14.9. Neural Networks (I)

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

14.10. Neural Networks (II)

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

Module 15. Artificial Intelligence. Strategies and Applications

15.1. Financial Services

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

15.2. Implications of Artificial Intelligence in Healthcare Service

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

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

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

15.4. Retail

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

15.5. Industry

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

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

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

15.7. Public Administration

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

15.8. Educational

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

15.9. Forestry and Agriculture

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

15.10. Human Resources

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

Module 16. Artificial Intelligence Applications in Digital Marketing and E-Commerce

16.1. Artificial Intelligence in Digital Marketing and E-Commerce

16.1.1. Content Personalization and Recommendations with Adobe Sensei
16.1.2. Audience Segmentation and Market Analysis
16.1.3. Predicting Trends and Buying Behavior

16.2. Digital Strategy with Optimizely

16.2.1. Incorporation of AI in Strategic Planning
16.2.2. Process Automation
16.2.3. Strategic Decisions

16.3. Continuous Adaptation to Changes in the Digital Environment

16.3.1. Strategy for the Management of Change
16.3.2. Adaptation of Marketing Strategies
16.3.3. Innovation

16.4. Content Marketing and Artificial Intelligence with Hub Spot

16.4.1. Content Personalization
16.4.2. Title and Description Optimization
16.4.3. Advanced Audience Segmentation
16.4.4. Sentiment Analysis
16.4.5. Content Marketing Automation

16.5. Automatic Content Generation

16.5.1. Content Optimization for SEO
16.5.2. Engagement
16.5.3. Analysis of Feelings and Emotions in the Content

16.6. AI in Inbound Marketing Strategies with Evergage

16.6.1. Growth Strategies based on Artificial Intelligence
16.6.2. Identifying Content and Distribution Opportunities
16.6.3. Use of Artificial Intelligence in the Identification of Business Opportunities

16.7. Automation of Workflows and Lead Tracking with Segment

16.7.1. Data Collection
16.7.2. Lead Segmentation and Lead Scoring
16.7.3. Multichannel Follow-up
16.7.4. Analysis and Optimization

16.8. Personalizing User Experiences Based on the Buying Cycle with Ortto

16.8.1. Personalized Content
16.8.2. User Experience Automation and Optimization
16.8.3. Retargeting

16.9. Artificial Intelligence and Digital Entrepreneurship

16.9.1. Growth Strategies based on Artificial Intelligence
16.9.2. Advanced Data Analysis
16.9.3. Price Optimization
16.9.4. Sector-specific Applications

16.10. Artificial Intelligence Applications for Startups and Emerging Companies

16.10.1. Challenges and Opportunities
16.10.2. Sector-specific Applications
16.10.3. Integration of Artificial Intelligence into Existing Products

Module 17. Campaign Optimization and AI Application

17.1. Artificial Intelligence and Personalized Advertising with Emarsys

17.1.1. Accurate Audience Targeting Using Algorithms
17.1.2. Product and Service Recommender
17.1.3. Conversion Funnel Optimization

17.2. Advanced Ad Targeting and Segmentation with Eloqua

17.2.1. Segmentation by Custom Audience Segments
17.2.2. Targeting by Devices and Platforms
17.2.3. Segmentation by Customer Lifecycle Stages

17.3. Optimization of Advertising Budgets by means of Artificial Intelligence

17.3.1. Continuous Optimization based on Data
17.3.2. Use of Real-time Ad Performance Data
17.3.3. Segmentation and Targeting

17.4. Automated Creation and Distribution of Personalized Advertisements with Cortex

17.4.1. Generation of Dynamic Creativities
17.4.2. Content Personalization
17.4.3. Optimization of Creative Design

17.5. Artificial Intelligence and Optimization of Marketing Campaigns with Adobe Target

17.5.1. Multiplatform Distribution
17.5.2. Frequency Optimization
17.5.3. Automated Tracking and Analysis

17.6. Predictive Analytics for Campaign Optimization

17.6.1. Prediction of Market Trends
17.6.2. Estimating Campaign Performance
17.6.3. Budget Optimization

17.7. Automated and Adaptive A/B Testing

17.7.1. Automated A/B Testing
17.7.2. Identification of High Value Audiences
17.7.3. Optimization of Creative Content

17.8. Real-time Data-driven Optimization with Evergage

17.8.1. Real-time Tuning
17.8.2. Customer Life Cycle Forecasting
17.8.3. Detection of Behavioral Patterns

17.9. Artificial Intelligence in SEO and SEM with BrightEdge

17.9.1. Keyword Analysis using Artificial Intelligence
17.9.2. Advanced Audience Targeting with Artificial Intelligence Tools
17.9.3. Ad Personalization using Artificial Intelligence

17.10. Automating Technical SEO Tasks and Keyword Analysis with Spyfu

17.10.1. Multichannel Attribution Analysis
17.10.2. Campaign Automation using Artificial Intelligence
17.10.3. Automatic Optimization of the Web Site Structure thanks to Artificial Intelligence

Module 18. Artificial Intelligence and User Experience in Digital Marketing

18.1. Personalization of the User Experience based on Behavior and Referrals

18.1.1. Personalization of Content thanks to Artificial Intelligence
18.1.2. Virtual Assistants and Chatbots with Cognigy
18.1.3. Intelligent Recommendations

18.2. Optimization of Web Site Navigation and Usability using Artificial Intelligence

18.2.1. Optimization of the User Interface
18.2.2. Predictive Analysis of User Behavior
18.2.3. Automation of Repetitive Processes

18.3. Virtual Assistance and Automated Customer Support with Dialogflow

18.3.1. Artificial Intelligence Sentiment and Emotion Analysis
18.3.2. Problem Detection and Prevention
18.3.3. Automation of Customer Support with Artificial Intelligence

18.4. Artificial Intelligence and Personalization of the Customer Experience with Zendesk Chat

18.4.1. Personalized Product Recommender
18.4.2. Personalized Content and Artificial Intelligence
18.4.3. Personalized communication

18.5. Real-time Customer Profiling

18.5.1. Personalized Offers and Promotions
18.5.2. User Experience Optimization
18.5.3. Advanced Audience Segmentation

18.6. Personalized Offers and Product Recommendations

18.6.1. Tracking and Retargeting Automation
18.6.2. Personalized Feedback and Surveys
18.6.3. Customer Service Optimization

18.7. Customer Satisfaction Tracking and Forecasting

18.7.1. Sentiment Analysis with Artificial Intelligence Tools
18.7.2. Tracking of Key Customer Satisfaction Metrics
18.7.3. Feedback Analysis with Artificial Intelligence Tools

18.8. Artificial Intelligence and Chatbots in Customer Service with Ada Support

18.8.1. Detection of Dissatisfied Customers
18.8.2. Predicting Customer Satisfaction
18.8.3. Personalization of Customer Service with Artificial Intelligence

18.9. Development and Training of Chatbots for Customer Service with Intercom

18.9.1. Automation of Surveys and Satisfaction Questionnaires
18.9.2. Analysis of Customer Interaction with the Product/Service
18.9.3. Real-time Feedback Integration with Artificial Intelligence

18.10. Automation of Responses to Frequent Inquiries with Chatfuel

18.10.1. Competitive Analysis
18.10.2. Feedbacks and Responses
18.10.3. Generation of Queries/Responses with Artificial Intelligence Tools

Module 19. Analyzing Digital Marketing Data with Artificial Intelligence

19.1. Artificial Intelligence in Data Analysis for Marketing with Google Analytics

19.1.1. Advanced Audience Segmentation
19.1.2. Predictive Trend Analysis using Artificial Intelligence
19.1.3. Price Optimization using Artificial Intelligence Tools

19.2. Automated Processing and Analysis of Large Data Volumes with RapidMiner

19.2.1. Brand Sentiment Analysis
19.2.2. Marketing Campaign Optimization
19.2.3. Personalization of Content and Messages with Artificial Intelligence Tools

19.3. Detection of Hidden Patterns and Trends in Marketing Data

19.3.1. Detection of Behavioral Patterns
19.3.2. Trend Detection using Artificial Intelligence
19.3.3. Marketing Attribution Analysis

19.4. Data-driven Insights and Recommendations Generation with Data Robot

19.4.1. Predictive Analytics Thanks to Artificial Intelligence
19.4.2. Advanced Audience Segmentation
19.4.3. Personalized Recommendations

19.5. Artificial Intelligence in Predictive Analytics for Marketing with Sisense

19.5.1. Price and Offer Optimization
19.5.2. Artificial Intelligence Sentiment and Opinion Analysis
19.5.3. Automation of Reports and Analysis

19.6. Prediction of Campaign Results and Conversions

19.6.1. Anomaly Detection
19.6.2. Customer Experience Optimization
19.6.3. Impact Analysis and Attribution

19.7. Risk and Opportunity Analysis in Marketing Strategies

19.7.1. Predictive Analysis in Market Trends
19.7.2. Evaluation of Competence
19.7.3. Reputational Risk Analysis

19.8. Sales and Product Demand Forecasting with ThoughtSpot

19.8.1. Return on Investment (ROI) Optimization
19.8.2. Compliance Risk Analysis
19.8.3. Innovation Opportunities

19.9. Artificial Intelligence and Social Media Analytics with Brandwatch

19.9.1. Market Niches and their Analysis with Artificial Intelligence
19.9.2. Monitoring Emerging Trends

19.10. Sentiment and Emotion Analysis on Social Media with Clarabridge

19.10.1. Identification of Influencers and Opinion Leaders
19.10.2. Brand Reputation Monitoring and Crisis Detection

Module 20. Artificial Intelligence to Automate E-Commerce Processes

20.1. E-Commerce Automation with Algolia

20.1.1. Customer Service Automation
20.1.2. Price Optimization
20.1.3. Personalization of Product Recommendations

20.2. Automation of Purchasing and Inventory Management Processes with Shopify Flow

20.2.1. Inventory and Logistics Management
20.2.2. Fraud Detection and Fraud Prevention
20.2.3. Sentiment Analysis

20.3. Integration of Artificial Intelligence in the Conversion Funnel

20.3.1. Sales and Performance Data Analysis
20.3.2. Data Analysis at the Awareness Stage
20.3.3. Data Analysis at the Conversion Stage

20.4. Chatbots and Virtual Assistants for Customer Service

20.4.1. Artificial Intelligence and 24/7 Assistance
20.4.2. Feedbacks and Responses
20.4.3. Generation of Queries/Responses with Artificial Intelligence Tools

20.5. Real-time Price Optimization and Product Recommender thanks to Artificial Intelligence with the Google Cloud AI Platform

20.5.1. Competitive Price Analysis and Segmentation
20.5.2. Dynamic Price Optimization
20.5.3. Price Sensitivity Forecasting

20.6. Fraud Detection and Prevention in e-Commerce Transactions with Sift

20.6.1. Anomaly Detection with the Help of Artificial Intelligence
20.6.2. Identity Verification
20.6.3. Real-time Monitoring with Artificial Intelligence
20.6.4. Implementation of Automated Rules and Policies

20.7. Artificial Intelligence Analysis to Detect Suspicious Behavior

20.7.1. Analysis of Suspicious Patterns
20.7.2. Behavioral Modeling with Artificial Intelligence Tools
20.7.3. Real-time Fraud Detection

20.8. Ethics and Responsibility in the Use of Artificial Intelligence in E-Commerce

20.8.1. Transparency in the Collection and Use of Data Using Artificial Intelligence Tools with Watson
20.8.2. Data Security
20.8.3. Responsibility for Design and Development with Artificial Intelligence

20.9. Automated Decision Making with Artificial Intelligence with Watson Studio

20.9.1. Transparency in the Decision-Making Process
20.9.2. Accountability for Results
20.9.3. Social Impact

20.10. Future Trends in Artificial Intelligence in the Field of Marketing and E-Commerce with REkko

20.10.1. Marketing and Advertising Automation
20.10.2. Predictive and Prescriptive Analytics
20.10.3. Visual e-Commerce and Search
20.10.4. Virtual Shopping Assistants

Make the most of this opportunity to surround yourself with expert professionals and learn from their work methodology”