AI Specialization Course
AI specialization course for ICT professionals
✨ Highlights
- Comprehensive training: From theoretical foundations to professional implementation
- University certification: Backed by 5 Valencian universities
Basic ML
Linear regression, SVM, Decision trees
Deep Learning
Neural networks, CNN, RNN
Foundation Models
LLMs, Generative models
NLP
Natural language processing
RAG Systems
Vector Databases, LangChain
MLOps
Docker, Azure ML, Apache Spark
After starting my journey in development, several fields caught my interest, especially artificial intelligence. This interest intensified with the arrival of large language models to the general public, at which point I decided I needed to acquire the necessary skills not just to leverage these technologies as a user, but to develop deep knowledge that would allow me to go further and understand the context holistically.
After evaluating different options, I chose valGrai (Valencian Graduate School and Research Network of Artificial Intelligence), a non-profit foundation established by the Valencian Government, the five public Valencian universities (UV, UPV, UA, UJI, and UMH), and industry companies, which coordinates training and research in artificial intelligence. The backing of professors from the leading Valencian universities was decisive in my decision.
This program provided me with comprehensive training in artificial intelligence and machine learning, structured from theoretical foundations to the implementation of professional solutions. I began by mastering the basic principles of AI and machine learning, including techniques such as linear regression, SVM, and decision trees, as well as handling the main Python libraries for ML. I progressed toward more sophisticated architectures, from basic neural networks to advanced models like CNN for image processing and RNN for temporal sequence analysis.
The program also covered the latest innovations, including transformers, large language models, and generative networks, allowing me to understand both discriminative and generative AI. I explored applications in natural language processing, speech recognition, and multimodal content generation, including creating images and videos through diffusion models.
Finally, I developed essential skills in MLOps to manage the complete lifecycle of ML projects, model deployment techniques as services using Docker, integration with Big Data through Apache Spark, and implementation of scalable cloud solutions with Microsoft Azure ML. The training culminated in a final project that integrated all this knowledge, consolidating an educational experience that provided me with the tools needed to tackle real challenges in the field of artificial intelligence.