You will integrate Artificial Intelligence in Digital Marketing to boost your brand's ability to connect more effectively with your target audience, all through TECH's revolutionary Relearning methodology" 

The use of Artificial Intelligence in Digital Marketing offers the opportunity to analyze large volumes of data in real time, allowing marketers to gain deep insights into user behavior, facilitating informed decision making. It also enables the creation of more accurate audience profiles, allowing for more effective segmentation and the delivery of personalized content, which significantly improves the user experience

This professional master’s degree, in which students will address content personalization and recommendations with Adobe Sensei, audience segmentation, market analysis, trend prediction and buying behaviors. In addition, it will cover campaign optimization and the application of AI in personalized advertising, advanced ad targeting, ad budget optimization and predictive analytics for campaign optimization

It will also delve into the integration of Artificial Intelligence to personalize the user experience in Digital Marketing, including optimization of website navigation and usability, virtual assistance and automated customer service. Finally, advanced data analysis techniques will be explored, including advanced audience segmentation, the processing and automated analysis of large volumes of data, the generation of insights and recommendations based on data, and the prediction of campaign results and conversions

This 100% online university program will provide graduates with the ease of being able to study it comfortably, wherever and whenever they want. Therefore, they will only need a device with an Internet connection to access all the contents. All this under the guidance of Relearning methodology, consisting in the reiteration of the key concepts for an optimal assimilation of the syllabus. It is a modality according to the current time, with all the guarantees to position the Marketing professional in a highly demanded sector

You'll implement machine learning algorithms to optimize ad campaigns, automatically adjusting targeting and budget to maximize ROI"

This professional master’s degree in Artificial Intelligence in Digital Marketing 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 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 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

Enroll now! You will access a complete and specialized qualification in leveraging the most advanced Artificial Intelligence tools and techniques in the field of Marketing and eCommerce"

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

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

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

You will delve into the creation of real-time customer profiles, as well as the generation of personalized offers and product recommendations, through an extensive library of innovative multimedia resources"

You will delve into ethics and responsibility in the use of AI in eCommerce and prepare yourself to meet the challenges and seize the emerging opportunities in this ever-evolving field"

The main objective of the professional master’s degree is to provide graduates with comprehensive and specialized education in the use of the latest Artificial Intelligence technologies to optimize Marketing and E-Commerce strategies. Through a practical and results-oriented approach, the program will prepare professionals to effectively apply AI tools and techniques to personalize content, improve audience segmentation, predict trends and buying behavior, optimize advertising campaigns, automate processes, and offer highly personalized user experiences.

You will develop practical skills for the implementation and management of AI tools and platforms to carry out your Digital Marketing campaigns"

General Objectives

• Understand the theoretical foundations of Artificial Intelligence
• Study the different types of data and understand the data lifecycle
• Evaluate the crucial role of data in the development and implementation of AI solutions
• Delve into algorithms and complexity to solve specific problems
• Explore the theoretical basis of neural networks for Deep Learning development
• Explore bio-inspired computing and its relevance in the development of intelligent systems
• Implement Artificial Intelligence applications in Digital Marketing and e-commerce to improve the efficiency and effectiveness of strategies
• Improve user experience in Digital Marketing by using Artificial Intelligence for dynamic personalization of websites, applications and content
• Implement Artificial Intelligence systems for the automation of e-Commerceprocesses, from inventory management to customer service
• Research and apply predictive AI models to identify emerging trends in the marketplace and anticipate customer needs
• Collaborate on cross-functional projects to integrate Artificial Intelligence solutions with existing Digital Marketing platforms and develop new strategies
• Evaluate the impact of Artificial Intelligence technologies on Digital Marketing and e-commerce, both from a business and ethical perspective

Specific Objectives

Module 1. Fundamentals of Artificial Intelligence 

• Analyze the historical evolution of Artificial Intelligence, from its beginnings to its current state, identifying key milestones and developments
• Understand the functioning of neural networks and their application in learning models in Artificial Intelligence
• Study the principles and applications of genetic algorithms, analyzing their usefulness in solving complex problems
• Analyze the importance of thesauri, vocabularies and taxonomies in structuring and processing data for Artificial Intelligence systems

Module 2. Data Types and Data Life Cycle 

• Understand the fundamental concepts of statistics and their application in data analysis
• Identify and classify the different types of statistical data, from quantitative to qualitative data
• Analyze the life cycle of data, from generation to disposal, identifying key stages
• Explore the initial stages of the data life cycle, highlighting the importance of data planning and structure
• Study data collection processes, including methodology, tools and collection channels
• Explore the Datawarehouse concept, with emphasis on the elements that comprise it and its design

Module 3. Data in Artificial Intelligence 

• Master the fundamentals of data science, covering tools, types and sources for information analysis
• Explore the process of transforming data into information using data mining and visualization techniques
• Study the structure and characteristics of datasets, understanding their importance in the preparation and use of data for Artificial Intelligence models
• Use specific tools and best practices in data handling and processing, ensuring efficiency and quality in the implementation of Artificial Intelligence

Module 4. Data Mining: Selection, Preprocessing and Transformation

• Master the techniques of statistical inference to understand and apply statistical methods in data mining
• Perform detailed exploratory analysis of data sets to identify relevant patterns, anomalies, and trends
• Develop skills for data preparation, including data cleaning, integration, and formatting for use in data mining
• Implement effective strategies for handling missing values in datasets, applying imputation or elimination methods according to context
• Identify and mitigate noise present in data, using filtering and smoothing techniques to improve the quality of the data set
• Address data preprocessing in Big Data environments

Module 5. Algorithm and Complexity in Artificial Intelligence 

• Introduce algorithm design strategies, providing a solid understanding of fundamental approaches to problem solving
• Analyze the efficiency and complexity of algorithms, applying analysis techniques to evaluate performance in terms of time and space
• Study and apply sorting algorithms, understanding their performance and comparing their efficiency in different contexts
• Explore tree-based algorithms, understanding their structure and applications
• Investigate algorithms with Heaps, analyzing their implementation and usefulness in efficient data manipulation
• Analyze graph-based algorithms, exploring their application in the representation and solution of problems involving complex relationships
• Study Greedyalgorithms, understanding their logic and applications in solving optimization problems
• Investigate and apply the backtracking technique for systematic problem solving, analyzing its effectiveness in various scenarios

Module 6. Intelligent Systems 

• Explore agent theory, understanding the fundamental concepts of its operation and its application in Artificial Intelligence and software engineering
• Study the representation of knowledge, including the analysis of ontologies and their application in the organization of structured information
• Analyze the concept of the semantic web and its impact on the organization and retrieval of information in digital environments
• Evaluate and compare different knowledge representations, integrating these to improve the efficiency and accuracy of intelligent systems

Module 7: Machine Learning and Data Mining

• Introduce the processes of knowledge discovery and the fundamental concepts of machine learning
• Study decision trees as supervised learning models, understanding their structure and applications
• Evaluate classifiers using specific techniques to measure their performance and accuracy in data classification
• Study neural networks, understanding their operation and architecture to solve complex machine learning problems
• Explore Bayesian methods and their application in machine learning, including Bayesian networks and Bayesian classifiers
• Analyze regression and continuous response models for predicting numerical values from data
• Study clustering techniques to identify patterns and structures in unlabeled data sets
• Explore text mining and natural language processing (NLP), understanding how machine learning techniques are applied to analyze and understand text

Module 8. Neural Networks, the Basis of Deep Learning 

• Master the fundamentals of Deep Learning, understanding its essential role
• Explore the fundamental operations in neural networks and understand their application in model building
• Analyze the different layers used in neural networks and learn how to select them appropriately
• Understanding the effective linking of layers and operations to design complex and efficient neural network architectures
• Use trainers and optimizers to tune and improve the performance of neural networks
• Explore the connection between biological and artificial neurons for a deeper understanding of model design

Module 9. Deep Neural Networks Training 

• Solve gradient-related problems in deep neural network training
• Explore and apply different optimizers to improve the efficiency and convergence of models
• Program the learning rate to dynamically adjust the convergence speed of the model
• Understand and address overfitting through specific strategies during training
• Apply practical guidelines to ensure efficient and effective training of deep neural networks
• Implement Transfer Learning as an advanced technique to improve model performance on specific tasks
• Explore and apply Data Augmentation techniques to enrich datasets and improve model generalization
• Develop practical applications using Transfer Learning to solve real-world problems

Module 10. Model Customization and Training with TensorFlow 

• Master the fundamentals of TensorFlow and its integration with NumPy for efficient data management and calculations
• Customize models and training algorithms using the advanced capabilities of TensorFlow
• Explore the tfdata API to efficiently manage and manipulate datasets
• Implement the TFRecord format for storing and accessing large datasets in TensorFlow
• Use Keras preprocessing layers to facilitate the construction of custom models
• Explore the TensorFlow Datasets project to access predefined datasets and improve development efficiency
• Develop a Deep Learning  application with TensorFlow, integrating the knowledge acquired in the module
• Apply in a practical way all the concepts learned in building and training custom models with TensorFlow in real-world situations

Module 11. Deep Computer Vision with Convolutional Neural Networks 

• Understand the architecture of the visual cortex and its relevance in Deep Computer Vision
• Explore and apply convolutional layers to extract key features from images
• Implement clustering layers and their use in  Deep Computer Vision models with Keras
• Analyze various Convolutional Neural Network (CNN) architectures and their applicability in different contexts
• Develop and implement a CNN ResNet using the Keras library to improve model efficiency and performance
• Use pre-trained Keras models to leverage transfer learning for specific tasks
• Apply classification and localization techniques in Deep Computer Vision environments
• Explore object detection and object tracking strategies using Convolutional Neural Networks

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

• Developing skills in text generation using Recurrent Neural Networks (RNN)
• Apply RNNs in opinion classification for sentiment analysis in texts
• Understand and apply attentional mechanisms in natural language processing models
• Analyze and use Transformers models in specific NLP tasks
• Explore the application of Transformers models in the context of image processing and computer vision
• Become familiar with the Hugging Face  Transformers library for efficient implementation of advanced models
• Compare different Transformers libraries to evaluate their suitability for specific tasks
• Develop a practical application of NLP that integrates RNN and attention mechanisms to solve real-world problems

Module 13. Autoencoders, GANs, and Diffusion Models 

• Develop efficient representations of data using Autoencoders, GANs and Diffusion Models
• Perform PCA using an incomplete linear autoencoder to optimize data representation
• Implement and understand the operation of stacked autoencoders
• Explore and apply convolutional autoencoders for efficient visual data representations
• Analyze and apply the effectiveness of sparse automatic encoders in data representation
• Generate fashion images from the MNIST dataset using Autoencoders
• Understand the concept of Generative Adversarial Networks (GANs) and Diffusion Models
• Implement and compare the performance of Diffusion Models and GANs in data generation

Module 14. Bio-Inspired Computing  

• Introduce the fundamental concepts of bio-inspired computing
• Analyze space exploration-exploitation strategies in genetic algorithms
• Examine models of evolutionary computation in the context of optimization 
• Continue detailed analysis of evolutionary computation models 
• Apply evolutionary programming to specific learning problems
• Address the complexity of multi-objective problems in the framework of bio-inspired computing
• Explore the application of neural networks in the field of bio-inspired computing 
• Delve into the implementation and usefulness of neural networks in bio-inspired computing

Module 15. Artificial Intelligence: Strategies and Applications

• Develop strategies for the implementation of artificial intelligence in financial services
• Identify and assess the risks associated with the use of Artificial Intelligence in the health care setting
• Assess the potential risks associated with the use of Artificial Intelligence in industry
• Apply Artificial Intelligence techniques in industry to improve productivity
• Design artificial intelligence solutions to optimize processes in public administration
• Evaluate the implementation of Artificial Intelligence technologies in the education sector
• Apply artificial intelligence techniques in forestry and agriculture to improve productivity
• Optimize Human Resources processes through the strategic use of Artificial Intelligence

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

• Analyze how to implement content personalization and recommendations using Adobe Sensei in Digital Marketing and eCommerce strategies
• Automate strategic decision-making processes with Optimizely to optimize the performance of Digital Marketing campaigns
• Analyze sentiment and emotions in marketing content using Hub Spot to adapt strategies and improve effectiveness
• Identify content and distribution opportunities using Evergage to improve the effectiveness of Inbound Marketing strategies
• Automate workflows and lead tracking with Segment to improve operational efficiency and effectiveness of marketing strategies
• Personalize user experiences based on the buying cycle using Autopilot to improve customer retention and loyalty

Module 17. Campaign Optimization and AI Application

• Implement AI and personalized advertising with Emarsys to create highly personalized and targeted ads to specific audiences
• Apply advanced ad targeting and segmentation techniques with Eloqua to reach specific audiences at different stages of the customer lifecycle
• Optimize ad budgets using Artificial Intelligence to maximize ROI and campaign effectiveness
• Perform automated tracking and analysis of campaign results to make real-time adjustments and improve performance
• Implement automated and adaptive A/B testing to identify high-value audiences and optimize campaign creative content
• Automate technical SEO and keyword analysis tasks with Spyfu, using Artificial Intelligence to perform multi-channel attribution analysis

Module 18. Artificial Intelligence and User Experience in Digital Marketing

• Personalize user experience based on user behavior and preferences using Artificial Intelligence
• Optimize website navigation and usability using Artificial Intelligence, including predictive analytics of user behavior and process automation
• Implement personalized offers and product recommendations, automating tracking and retargeting, as well as customer service optimization
• Track and predict customer satisfaction using sentiment analysis with AI tools and tracking of key metrics
• Develop and train chatbots for customer service with Itercom, automating satisfaction surveys and questionnaires, as well as integrating real-time feedback 
• Automating responses to frequent queries with Chatfuel, including competitive analysis and AI query/response generation

Module 19. Analyzing Digital Marketing Data with Artificial Intelligence

• Detect hidden patterns and trends in mMrketing data and perform brand sentiment analysis
• Predict campaign and conversion results, detect anomalies and optimize customer experience using predictive analytics
• Perform risk and opportunity analysis on marketing strategies, including predictive analytics on market trends and competitor assessment
• Use AI and social media analytics with Brandwatch to identify market niches, monitor emerging trends and perform sentiment analysis

Module 20. Artificial Intelligence to Automate e-Commerce Processes

• Integrate Artificial Intelligence in the conversion funnel to analyze sales data and performance at all stages of the buying process
• Implement chatbots and virtual assistants for 24/7 customer service, using Artificial Intelligence to generate automatic responses and collect feedback
• Detect and prevent fraud in e-Commerce transactions with Sift, using AI to detect anomalies and verify identities
• Perform Artificial Intelligence analysis to detect suspicious behavior and fraudulent patterns in real-time
• Promote ethics and accountability in the use of Artificial Intelligence in e-Commerce, ensuring transparency in the collection and use of data
• Explore future trends of Artificial Intelligence in Marketing and E-Commerce with REkko

You will understand the ethical and responsible aspects of the use of AI in Digital Marketing, preparing you to anticipate and adapt to future trends in this dynamic field”