Expert Diploma in Applied AI & Machine Learning
Mastery of Industry-Leading Tools
This exclusive program has been carefully structured and designed to provide comprehensive training, combining advanced theoretical foundations with hands-on applications. Its goal is to help participants master every stage of artificial intelligence (AI) and machine learning (ML) systems, design, development, implementation, optimization, and management, tailored to high-impact business, technology, and organizational environments.
The Diploma includes specialized courses that focus on essential technical skills such as large-scale data processing, predictive algorithm training, process automation, and AI ethics. It also promotes expertise with industry-leading tools, including TensorFlow, PyTorch, and Azure Machine Learning, together with agile methodologies for tackling complex challenges in sectors such as healthcare, finance, logistics, manufacturing, and retail.
In addition, the program emphasizes collaborative frameworks, scalability strategies, and AI project governance. It is supported by a strategic partnership with Microsoft, which provides certified resources, access to cloud platforms, real-world case studies, and guidance on best industry practices. This ensures a seamless transition from academic theory to the effective implementation of innovative and sustainable solutions in today’s global marketplace.
This high-level management program is specifically designed to prepare professionals and future leaders with the practical ability to harness the transformative potential of AI across every aspect of an organization. It goes beyond technical understanding, focusing on how to strategically, operationally, and ethically integrate AI to drive innovation, efficiency, and value creation in any business area. The program develops hybrid managers who serve as the vital link between the technical power of AI and measurable business results.
In Collaboration With:
Highly Employable Training
Course Program 30 ECTS
- 6 ECTS | Database Management
- 6 ECTS | AI Applied to Business
- ECTS | Deep Learning and Neural Networks
- 12 ECTS | Final Project
In today’s data-driven business environment, this course provides a strategic perspective on how to structure, manage, and leverage databases as a key decision-making asset. Core concepts are covered to ensure the quality, availability, and usefulness of real-time information.
With a results-oriented approach, this course explores how artificial intelligence is redefining operational efficiency, customer experience, and value creation. Students analyze the real impact of AI across sectors and develop criteria for its effective implementation in business transformation projects.
This course examines advanced applications of deep learning in the corporate context to drive innovation and digital transformation. Through the study of neural networks and real-world cases, students identify opportunities to create competitive advantages through predictive solutions, intelligent automation, and complex data analysis.
The program concludes with an integrative final project, tailored to students’ individual interests and career goals. Options include:
• A business or consulting plan for a new or existing company
• An applied research project on a relevant topic related to the integration of AI in business
• Participation in the Venture Creation Lab, guided by business leaders and entrepreneurs, with the support of simulators. This option helps students build the skills and capabilities needed to design and launch innovative technology companies or startups.
More Academic Info
Competences
General Competences
- Mastery of ML Algorithms: Regression, classification, clustering, decision trees, ensemble methods (Random Forest, XGBoost), and optimization techniques.
- Deep Learning: Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Transformers, and frameworks such as TensorFlow, PyTorch, and Keras.
- Data Processing: Preprocessing, cleaning, normalization, and feature engineering.
- Predictive Modeling: Model evaluation (metrics such as precision, recall, F1 score, AUC-ROC), cross-validation, and hyperparameter tuning.
- Computer Vision: Object detection, semantic segmentation, and practical applications.
- Natural Language Processing (NLP): Language models (BERT, GPT), sentiment analysis, and chatbot development.
Specific Competences
- Real-World Problem Solving: Implementation of AI/ML solutions in sectors such as healthcare, finance, retail, logistics, and industry.
- System Integration: Deploying models in production environments (APIs, microservices) using tools such as Docker, Kubernetes, or Flask.
- AI Project Management: Agile methodologies, ML project lifecycles (CRISP-DM), and tools such as MLflow and DVC.
- Big Data Management: Use of platforms such as Apache Spark, Hadoop, or NoSQL databases.
- Data Visualization: Tools such as Tableau, Power BI, and libraries like Matplotlib, Seaborn, and Plotly.
- Ethics and Privacy: Addressing data bias, ensuring regulatory compliance (GDPR), and promoting model transparency (Explainable AI – XAI).
Methodology
The HyFlex methodology allows classes to be attended either remotely or in person through immersive classroom technology, where online students integrate seamlessly with on-campus students.
Teaching takes place on Friday afternoons and/or Saturday mornings in a hybrid format, making it easier to balance personal and professional commitments while maintaining the university’s hallmark excellence.
Awards to Global Campus Nebrija
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