Thanks to this 100% online Professional master’s degree, you will get the most out of Artificial Intelligence to optimize user experiences and personalize content”

Why Study at TECH?

TECH is the world's largest 100% online business school. It is an elite business school, with a model based on the highest academic standards. A world-class center for intensive managerial skills education.   

TECH is a university at the forefront of technology, and puts all its resources at the student's disposal to help them achieve entrepreneurial success"

At TECH Global University

Innovation

The university offers an online learning model that balances the latest educational technology with the most rigorous teaching methods. A unique method with the highest international recognition that will provide students with the keys to develop in a rapidly-evolving world, where innovation must be every entrepreneur’s focus.

"Microsoft Europe Success Story", for integrating the innovative, interactive multi-video system. 

The Highest Standards

Admissions criteria at TECH are not economic. Students don't need to make a large investment to study at this university. However, in order to obtain a qualification from TECH, the student's intelligence and ability will be tested to their limits. The institution's academic standards are exceptionally high...  

95% of TECH students successfully complete their studies.

Networking

Professionals from countries all over the world attend TECH, allowing students to establish a large network of contacts that may prove useful to them in the future.  

100,000+ executives prepared each year, 200+ different nationalities.

Empowerment

Students will grow hand in hand with the best companies and highly regarded and influential professionals. TECH has developed strategic partnerships and a valuable network of contacts with major economic players in 7 continents.  

500+ collaborative agreements with leading companies.

Talent

This program is a unique initiative to allow students to showcase their talent in the business world. An opportunity that will allow them to voice their concerns and share their business vision. 

After completing this program, TECH helps students show the world their talent.

Multicultural Context

While studying at TECH, students will enjoy a unique experience. Study in a multicultural context. In a program with a global vision, through which students can learn about the operating methods in different parts of the world, and gather the latest information that best adapts to their business idea.

TECH students represent more than 200 different nationalities.  

Learn with the best

In the classroom, TECH’s teaching staff discuss how they have achieved success in their companies, working in a real, lively, and dynamic context. Teachers who are fully committed to offering a quality specialization that will allow students to advance in their career and stand out in the business world. 

Teachers representing 20 different nationalities. 

TECH strives for excellence and, to this end, boasts a series of characteristics that make this university unique:   

Analysis

TECH explores the student’s critical side, their ability to question things, their problem-solving skills, as well as their interpersonal skills.  

Academic Excellence

TECH offers students the best online learning methodology. The university combines the Relearning method (postgraduate learning methodology with the best international valuation) with the Case Study. Tradition and vanguard in a difficult balance, and in the context of the most demanding educational itinerary. 

TECH is the world’s largest online university. It currently boasts a portfolio of more than 10,000 university postgraduate programs. And in today's new economy, volume + technology = a ground-breaking price. This way, TECH ensures that studying is not as expensive for students as it would be at another university.   

At TECH, you will have access to the most rigorous and up-to-date case analyses in academia” 

The Professional master’s degree in Artificial Intelligence in Digital Marketing is a program designed to guarantee flexibility, thanks to a convenient 100% online format that allows you to choose the time and place that best suits you to expand your knowledge. The program is developed over 12 months, in which you will live an academic experience that will raise your professional horizons to a higher level.

You will learn about the current situation of the labor market in Artificial Intelligence in Digital Marketing and multiply your chances of success thanks to TECH”

Syllabus

This program in Artificial Intelligence in Digital Marketing is an intensive program that will equip you with the necessary tools to make the most informed strategic decisions. In this way, graduates will use data and analytics to improve both the effectiveness and performance of advertising campaigns.

During 12 months of training, students will have access to top-quality teaching materials, developed by a faculty versed in Artificial Intelligence. In addition, the academic pathway will include a myriad of resources to reinforce key concepts, including case studies, specialized readings and interactive summaries.

This program will delve into the personalization of content using Adobe Sensei, as well as the prediction of trends and purchasing behavior. In this way, experts will stand out for having a comprehensive knowledge of Artificial Intelligence in Digital Marketing and will acquire a fully strategic perspective.

The curriculum will equip specialists with the necessary skills to successfully overcome the challenges that arise during the implementation of Artificial Intelligence in their various projects. To this end, the syllabus will provide state-of-the-art trends in areas such as Intelligent Systems, Machine Learning and Machine Learning. Therefore, graduates will be highly qualified to create innovative projects that stand out in the market.

This Professional master’s degree takes place over 12 months and is divided into 20 modules:

Module 1. Fundamentals of Artificial Intelligence
Module 2. Data Types and Life Cycle
Module 3. Data in Artificial Intelligence
Module 4. Data Mining: Selection, Pre-Processing and Transformation
Module 5. Algorithm and Complexity in Artificial Intelligence
Module 6. Intelligent Systems
Module 7. Machine Learning and Data Mining 
Module 8. Neural Networks, the Basis of Deep Learning
Module 9. Deep Neural Networks Training
Module 10. Model Customization and Training with TensorFlow
Module 11. Deep Computer Vision with Convolutional Neural Networks
Module 12. Natural Language Processing (NLP) with Recurrent Neural Networks (RNN) and Attention
Module 13. Autoencoders, GANs and Diffusion Models
Module 14. Bio-Inspired Computing
Module 15. Artificial Intelligence: Strategies and Applications
Module 16. Artificial Intelligence Applications in Digital Marketing and E-Commerce
Module 17. Campaign Optimization and AI Application
Module 18. Artificial Intelligence and User Experience in Digital Marketing
Module 19. Analyzing Digital Marketing Data with Artificial Intelligence
Module 20. Artificial Intelligence to Automate e-Commerce Processes

Where, When and How is it Taught?

TECH offers the possibility to develop this Professional master’s degree in Artificial Intelligence in Digital Marketing completely online. Throughout the 12 months of the educational program, the students will be able to access all the contents of this program at any time, allowing them to self-manage their study time.

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 Dialogue 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
2.3.2. 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 Result Evaluation

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

2.8. Datawarehouse

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

2.9. Data Availability

2.9.1. Access
2.9.2. Uses
2.9.3. Security

Module 3. Data in Artificial Intelligence

3.1. Data Science

3.1.1. Data Science
3.1.2. Advanced Tools for Data Scientists

3.2. Data, Information and Knowledge

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

3.3. From Data to Information

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

3.4. Extraction of Information Through Visualization

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

3.5. Data Quality

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

3.6. Dataset

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

3.7. Unbalance

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

3.8. Unsupervised Models

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

3.9. Supervised Models

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

3.10. Tools and Good Practices

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

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

4.1. Statistical Inference

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

4.2. Exploratory Analysis

4.2.1. Descriptive Analysis
4.2.2. Visualization
4.2.3. Data Preparation

4.3. Data Preparation

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

4.4. Missing Values

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

4.5. Noise in the Data

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

4.6. The Curse of Dimensionality

4.6.1. Oversampling
4.6.2. Undersampling
4.6.3. Multidimensional Data Reduction

4.7. From Continuous to Discrete Attributes

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

4.8. The Data

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

4.9. Instance Selection

4.9.1. Methods for Instance Selection 
4.9.2. Prototype Selection 
4.9.3. Advanced Methods for Instance Selection 
4.10. Data Pre-processing in Big Data Environments

Module 5. Algorithm and Complexity in Artificial Intelligence

5.1. Introduction to Algorithm Design Strategies

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

5.2. Efficiency and Analysis of Algorithms

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

5.3. Sorting Algorithms

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

5.4. Algorithms with Trees

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

5.5. Algorithms Using Heaps

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

5.6. Graph Algorithms

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

5.7. Greedy Algorithms

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

5.8. Minimal Path Finding

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

5.9. Greedy Algorithms on Graphs

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

5.10. Backtracking

5.10.1 Backtracking 
5.10.2. Alternative Techniques 

Module 6. Intelligent Systems

6.1. Agent Theory 

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

6.2. Agent Architectures 

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

6.3. Information and Knowledge 

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

6.4. Knowledge Representation 

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

6.5. Ontologies 

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

6.6. Ontology Languages and Ontology Creation Software 

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

6.7. Semantic Web 

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

6.8. Other Knowledge Representation Models 

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

6.9. Knowledge Representation Assessment and Integration 

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

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

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

Module 7. Machine Learning and Data Mining

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

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

7.2. Data Exploration and Pre-Processing 

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

7.3. Decision Trees 

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

7.4. Evaluation of Classifiers 

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

7.5. Classification Rules 

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

7.6. Neural Networks 

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

7.7. Bayesian Methods 

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

7.8. Regression and Continuous Response Models 

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

7.9. Clustering 

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

7.10. Text Mining and Natural Language Processing (NLP) 

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

Module 8. Neural Networks, the Basis of Deep Learning

8.1. Deep Learning  

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

8.2. Surgery  

8.2.1. Sum 
8.2.2. Product 
8.2.3. Transfer 

8.3. Layers  

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

8.4. Layer Bonding and Operations  

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

8.5. Construction of the First Neural Network 

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

8.6. Trainer and Optimizer 

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

8.7. Application of the Principles of Neural Networks 

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

8.8 From Biological to Artificial Neurons 

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

8.9. Implementation of MLP (Multilayer Perceptron) with Keras 

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

8.10. Fine Tuning Hyperparameters of Neural Networks 

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

Module 9. Deep Neural Networks Training

9.1. Gradient Problems 

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

9.2. Reuse of Pre-Trained Layers 

9.2.1. 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. Grap 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 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 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. 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 Application 
10.10.2. Building a Deep Learning App with TensorFlow 
10.10.3. Training a model 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 Cortex Visual 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. Classification and Localization in Deep Computer Vision 

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

11.9. Object Detection and Object Tracking 

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

11.10. Semantic Segmentation 

11.10.1. Deep Learning for Semantic Segmentation 
11.10.1. Edge Detection 
11.10.1. Segmentation Methods Based on Rules 

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

12.1. Text Generation using RNN 

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

12.2. Training Data Set Creation 

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

12.3. Classification of Opinions with RNN 

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

12.4. Encoder-Decoder Network for Neural Machine Translation 

12.4.1. Training an RNN for Machine Translation 
12.4.2. Use of an encoder-decoder network for machine translation 
12.4.3. Improving the Accuracy of Machine Translation with RNNs 

12.5. Attention Mechanisms 

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

12.6. Transformer models 

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

12.7. Transformers for vision 

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

12.8. Hugging Face Transformer 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 TransformersLibrary 

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. Trendy MNIST Image Generation 

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 Computing 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 Computation Models (I) 

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

14.6. Evolutionary Computation Models (II) 

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

14.7. Evolutionary Programming Applied to Learning Problems 

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

14.8. Multi-Objective Problems 

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

14.9. Neural Networks (I) 

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

14.10. Neural Networks (II) 

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

Module 15. Artificial Intelligence: Strategies and Applications

15.1. Financial Services

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

15.2. Implications of Artificial Intelligence in Healthcare Service

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

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

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

15.4. Retail

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

15.5. Industry

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

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

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

15.7. Public Administration

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

15.8. Educational

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

15.9. Forestry and Agriculture

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

15.10 Human Resources

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

Module 16. 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 Autopilot

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 Itercom

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

The teaching materials of this program, elaborated by these specialists, have contents that are completely applicable to your professional experiences"