This Master’s degree has been conceived to respond to the growing need for knowledge and skills in the development of AI models and algorithms demanded by today’s technology-driven market. As a matter of fact, many specialized staffing companies point to AI as one of the knowledge assets that will be the most demanded in the coming years, considering that the AI sector is expected to have a global business volume of 16 trillion dollars by 2030. This program has been designed to benefit all professional profiles, with an Introduction to AI basics that does not require extensive prior knowledge of programming and statistics. It has been structured in two large sections: first, a technical section which explores the main Machine Learning and Deep Learning models and algorithms, and the second section addresses its business applications and implications. Upon completion of the program, the students will have the necessary skills to manage and promote AI projects.
Objectives
The overall objective is to equip students with the necessary knowledge and tools to understand, manage and lead IA initiatives and projects in an organization.
The overall objective can be attained through the following specific objective:
1. Understand AI basic concepts, its limits, and possibilities.
2. Learn AI project programming languages, tools and platforms
3. Become familiar with the most used Machine Learning and Deep Learning algorithms
4. Analyze and learn other technologies to be able to develop innovative and differential business models.
5. Learn the main AI project management methodologies
6. Understand the implications and applications of AI in the different functional areas of a company
Methodology
At Educa PHAROS, we use a highly practical methodology focused on the direct application of knowledge in the student’s professional environment, combining theoretical content with real-world case studies, digital tools, and the guidance of a specialized teaching team.
Program
State of the art of artificial intelligence
Philosophy of artificial intelligence
Future of artificial intelligence
Project development process with artificial intelligence
Data, your greatest asset
Machine learning
Deep learning
Transformers
Generation of synthetic data
Hyperparameters in artificial intelligence models
Linear regression
Non-linear regression and support vector machines (SVM)
Decision trees, random forests
Fuse logic and gradient down
Recommendation systems
Preparation of the working environment: Anaconda, Visual Studio Code and Python
Input dataset and data preprocessing
TensorHub, TensorFlow and Keras
Image processing
Generation of artificial intelligence models
Introduction
Self - Service solutions
Data processing techniques
Data quality management
Types of data problem
Data cleaning with Excel
DATASET
Functions. Part I
Functions. Part II
Functions. Part III
Instructions for installing talend data preparation free desktop
Data Cleansing with Talend Data Preparation
Basic cleansing functions
Data normalization
Data enrichment
Registration instructions
Data cleansing with trifacta
Basic cleansing functions
Data normalization
Data enrichment
Introduction
Simple, multiple and logistic linear regression (I)
Simple, multiple and logistic linear regression (II)
Support vector machines (SVM)
Decision trees
KNN (k-nearest neighbors)
Naive Bayes
Evaluation of supervised models
Example exercise
Proposed exercise
Introduction to clustering: purconsider and metrics
K-means clustering
Hierarchical clustering, other techniques and examples
Principal component analysis (PCA)
PCA example exercise
Artificial Neural Networks (ANN) (I)
Artificial Neural Networks (ANN) (II)
Artificial Neural Networks (ANN) (III)
Example exercise
Proposed exercise
Introduction
Review: Artificial neural network (ANN)
Review: ANN exercises
Convolutional Neural Networks (CNN)
CNN Exercises
Natural language processing (I)
Recurrent neural networks (RNN) (I)
Recurrent neural networks (RNN) (II)
Natural language processing (II)
RNN Exercise
Boltzmann Machines (BM)
Restricted Boltzmann Machines (RBM)
Recommender systems
Recommender systems: metrics
RBM exercise
Self-organizing maps (SOM)
SOM exercises
Autoencoders (AE)
AE exercises
Proposed exercise
Introduction to Power BI
Different types of Power BI
First simple report
Power Query: Data Sources
Data Transformation
Data Modeling
Starting with DAX (I)
Starting with DAX (II)
Getting proficient in DAX (I)
Getting proficient in DAX (II)
Table and Matrix
Trends
How to properly filter your data
Bookmarks
Drill through
Understanding Power BI Service in depth
Sharing content in Power BI Service
Comparing Power BI Service and Power BI Report Server
Integrating Python and R in Power BI Desktop
Introducing Bravo for Power BI Desktop
Linear regression
Logistic regression
Basic Neural Network
Clustering
Principal Component Analysis (PCA)
Deep learning
Optimization
Convolutional Neural Network
Recurrent Neural Network
Natural Language Processing (NLP)
Creating tables and Reports
Transformation and filtering data
Data visualization
Relation between data tables
Dashboard
Object detection in images
Object classification in images
Facial recognition
Word detection
Business Intelligence application
The fourth industrial revolution
Digital transformation in companies
Fundaments and key points
Benefits
Enabling technologies
Big data
Cloud computing
Blockchain
Artificial intelligence
Augmented and virtual reality
BIM
Collaborative robots
Additive manufacturing
Hyperconnectivity
IoT
Manufacturing execution system (MES)
Process integration and efficiency
Use cases
New methodologies: agile, lean startup or design thinking
Business change management
Introduction
Key elements in AI project management
AI project characteristics
Introduction to the main agile and ideation methodologies
Educa PHAROS is a training solution for businesses that centralizes learning, simplifies user management, and allows progress to be tracked through dashboards and reports.
It is designed for organizations that want to develop internal skills, standardize role-based training, and have traceability and learning metrics.
It includes learning paths by profile, user and permission management, progress reports, certification, and access to masterclasses or live sessions (depending on the plan).
It is based on best practices in security, access control, learning tracking, and implementation support. Customize this text according to your commitments (SLA, compliance, etc.).
Educa PHAROS is a next-generation training model that places a company’s human capital at the forefront. Through a platform that adapts to each company’s corporate image and offers a total of more than 900 courses, it provides training tailored to each organization’s specific needs. The unlimited flat-rate plan provides each company with the number of courses that best suits its needs, as well as the ability to determine which employees will have access.
Digital Transformation and Smart Infrastructure 
English 
Credits: 60 ECTS
Leadership and Management 
Spanish
60 ECTS