Machine Learning Training in Évora

This instructor-led, live training in Évora (online or on-site) is designed for advanced-level AI researchers, data scientists, and security specialists seeking to implement federated learning techniques to train AI models across multiple edge devices while preserving data privacy.

Upon completing this training, participants will be able to:

• Grasp the principles and advantages of federated learning within Edge AI.
• Develop federated learning models using TensorFlow Federated and PyTorch.
• Enhance AI training performance across distributed edge devices.
• Tackle data privacy and security challenges inherent in federated learning.
• Deploy and oversee federated learning systems in practical applications.

This instructor-led, live training in Évora (online or onsite) is aimed at advanced-level computer vision engineers and AI developers who wish to fine-tune VLMs such as CLIP and Flamingo to improve performance on industry-specific visual-text tasks.

By the end of this training, participants will be able to:

• Understand the architecture and pretraining methods of vision-language models.
• Fine-tune VLMs for classification, retrieval, captioning, or multimodal QA.
• Prepare datasets and apply PEFT strategies to reduce resource usage.
• Evaluate and deploy customized VLMs in production environments.

This instructor-led, live training in Évora (online or onsite) is designed for intermediate-level NLP engineers and knowledge management teams who wish to fine-tune RAG pipelines to enhance performance in question answering, enterprise search, and summarization use cases.

By the end of this training, participants will be able to:

• Understand the architecture and workflow of RAG systems.
• Fine-tune retriever and generator components for domain-specific data.
• Evaluate RAG performance and apply improvements through PEFT techniques.
• Deploy optimized RAG systems for internal or production use.

This instructor-led, live training in Évora (online or onsite) is aimed at beginner-level professionals who wish to understand the concept of pre-trained models and learn how to apply them to solve real-world problems without building models from scratch.

By the end of this training, participants will be able to:

• Understand the concept and benefits of pre-trained models.
• Explore various pre-trained model architectures and their use cases.
• Fine-tune a pre-trained model for specific tasks.
• Implement pre-trained models in simple machine learning projects.

This instructor-led, live training in Évora (online or onsite) is designed for intermediate-level AI developers, machine learning engineers, and system architects who wish to optimize AI models for edge deployment.

Upon completion of this training, participants will be capable of:

• Grasping the challenges and prerequisites associated with deploying AI models on edge devices.
• Applying model compression strategies to decrease the size and complexity of AI models.
• Leveraging quantization approaches to boost model efficiency on edge hardware.
• Executing pruning and other optimization techniques to enhance model performance.
• Deploying refined AI models across various edge devices.

This live, instructor-led training in Évora (available online or onsite) targets intermediate-level developers, data scientists, and tech enthusiasts aiming to acquire practical skills in deploying AI models on edge devices for diverse applications.

By the end of the program, participants will be able to:

• Understand the fundamentals of Edge AI and its key benefits.
• Set up and configure the edge computing environment.
• Develop, train, and optimize AI models for edge deployment.
• Implement practical AI solutions on edge devices.
• Evaluate and improve the performance of edge-deployed models.
• Address ethical and security considerations in Edge AI applications.

This instructor-led, live training in Évora (online or onsite) is aimed at advanced-level professionals who wish to master the technologies behind autonomous systems.

By the end of this training, participants will be able to:

• Design and implement AI models for autonomous decision-making.
• Develop control algorithms for autonomous navigation and obstacle avoidance.
• Ensure safety and reliability in AI-powered autonomous systems.
• Integrate autonomous systems with existing robotics and AI frameworks.

This instructor-led, live training in Évora (online or onsite) is designed for advanced-level professionals seeking to deepen their understanding of machine learning models, enhance their hyperparameter tuning skills, and master effective model deployment techniques using Google Colab.

Upon completion of this training, participants will be able to:

• Build advanced machine learning models using established frameworks such as Scikit-learn and TensorFlow.
• Enhance model performance through systematic hyperparameter tuning.
• Deploy machine learning models into real-world applications via Google Colab.
• Collaborate and oversee large-scale machine learning initiatives within Google Colab.

This instructor-led, live training in Évora (online or onsite) is tailored for intermediate-level professionals who wish to apply AI techniques to optimize yield management in semiconductor manufacturing.

By the end of this training, participants will be able to:

• Analyze production data to identify factors affecting yield rates.
• Implement AI algorithms to enhance yield management processes.
• Optimize production parameters to reduce defects and improve yields.
• Integrate AI-driven yield management into existing production workflows.

This instructor-led, live training in Évora (online or onsite) is aimed at advanced-level professionals who wish to deploy fine-tuned models reliably and efficiently.

By the end of this training, participants will be able to:

• Understand the challenges of deploying fine-tuned models into production.
• Containerize and deploy models using tools like Docker and Kubernetes.
• Implement monitoring and logging for deployed models.
• Optimize models for latency and scalability in real-world scenarios.

This instructor-led, live training in Évora (online or onsite) targets intermediate-level business and AI professionals who wish to apply machine learning in business, forecasting, and AI-driven systems using real case studies and Python-based tools.

Upon completing this training, participants will be able to:

• Grasp how machine learning aligns with AI and business strategy.
• Apply supervised and unsupervised learning techniques to solve structured business problems.
• Preprocess and transform data for modelling.
• Utilise neural networks for classification and prediction tasks.
• Conduct sales forecasting using both statistical and ML-based methods.
• Implement clustering and association rule mining for customer segmentation and pattern discovery.

This 21-hour instructor-led course in Évora equips intermediate professionals to implement TinyML for smart agriculture. Gain skills in building lightweight models, integrating edge AI with IoT, and optimizing systems for precision irrigation and pest detection through hands-on lab activities.

This instructor-led, live training in Évora (online or onsite) is targeted at advanced professionals who wish to apply cutting-edge AI techniques to semiconductor design automation, enhancing efficiency, accuracy, and innovation in chip design and verification.

By the conclusion of this training, participants will be able to:

• Apply advanced AI techniques to optimise semiconductor design processes.
• Integrate machine learning models into EDA tools for improved design verification.
• Develop AI-driven solutions for complex design challenges in chip fabrication.
• Leverage neural networks to enhance the accuracy and speed of design automation.

This instructor-led, live training in Évora (online or onsite) is aimed at intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.

By the end of this training, participants will be able to:

• Set up and navigate Google Colab for deep learning projects.
• Understand the fundamentals of neural networks.
• Implement deep learning models using TensorFlow.
• Train and evaluate deep learning models.
• Utilize advanced features of TensorFlow for deep learning.

This live, instructor-led training in Évora focuses on the implementation of TinyML solutions for health monitoring and diagnostics. Learners will master the design of models for real-time physiological data, optimization strategies for low-power wearables, and methods to guarantee clinical reliability. The curriculum includes practical laboratory exercises.

This instructor-led, live training in Évora (online or onsite) targets intermediate-level professionals who wish to understand and apply AI techniques for optimizing semiconductor fabrication processes.

Upon completing this training, participants will be capable of:

• Grasping AI methodologies used for process optimization in chip fabrication.
• Deploying AI models to boost yield and minimize defects.
• Analyzing process data to pinpoint critical parameters for optimization.
• Leveraging machine learning techniques to fine-tune semiconductor manufacturing workflows.

This instructor-led live training in Évora (online or onsite) is designed for intermediate-level participants who wish to automate and manage machine learning workflows, including model training, validation, and deployment using Apache Airflow.

Upon completion of this training, participants will be capable of:

• Configuring Apache Airflow to orchestrate machine learning workflows.
• Automating tasks related to data preprocessing, model training, and validation.
• Integrating Airflow with various machine learning frameworks and tools.
• Deploying machine learning models through automated pipelines.
• Monitoring and optimising machine learning workflows in production environments.

This instructor-led, live training in Évora (online or onsite) is aimed at intermediate-level data scientists and developers who wish to apply machine learning algorithms efficiently using the Google Colab environment.

By the end of this training, participants will be able to:

• Set up and navigate Google Colab for machine learning projects.
• Understand and apply various machine learning algorithms.
• Use libraries like Scikit-learn to analyze and predict data.
• Implement supervised and unsupervised learning models.
• Optimize and evaluate machine learning models effectively.

This instructor-led, live training in Évora (online or onsite) is aimed at data scientists and developers who wish to use ML.NET machine learning models to automatically derive projections from executed data analysis for enterprise applications.

By the end of this training, participants will be able to:

• Install ML.NET and integrate it into the application development environment.
• Understand the machine learning principles behind ML.NET tools and algorithms.
• Build and train machine learning models to perform predictions with the provided data smartly.
• Evaluate the performance of a machine learning model using the ML.NET metrics.
• Optimize the accuracy of the existing machine learning models based on the ML.NET framework.
• Apply the machine learning concepts of ML.NET to other data science applications.

This instructor-led, live training in Évora (online or onsite) is aimed at intermediate-level data professionals who wish to apply machine learning techniques to data-driven business problems, including sales forecasting and predictive modeling using neural networks.

By the end of this training, participants will be able to:

• Understand the core concepts and types of machine learning.
• Apply key algorithms for classification, regression, clustering, and association analysis.
• Perform exploratory data analysis and data preparation using Python.
• Use neural networks for nonlinear modeling tasks.
• Implement predictive analytics for business forecasting, including sales data.
• Evaluate and optimize model performance using visual and statistical techniques.

This instructor-led, live training in Évora (online or onsite) is designed for intermediate to advanced cybersecurity professionals aiming to enhance their skills in AI-driven threat detection and incident response.

By the end of this training, participants will be able to:

• Implement advanced AI algorithms for real-time threat detection.
• Customize AI models to address specific cybersecurity challenges.
• Develop automation workflows for threat response.
• Secure AI-driven security tools against adversarial attacks.

This instructor-led, live training in Évora (online or onsite) is designed for beginner-level cybersecurity professionals seeking to harness AI to enhance their threat detection and response capabilities.

Upon completion of this training, participants will be able to:

• Comprehend the role of AI in cybersecurity.
• Deploy AI algorithms for effective threat detection.
• Automate incident response processes using AI tools.
• Integrate AI solutions into current cybersecurity infrastructure.

This eight-day programme offers a comprehensive journey, bridging robust Python engineering foundations with advanced AI system design. Participants cultivate disciplined coding habits, master statistical and deep learning methodologies, and construct production-ready generative AI and agent-based systems. The curriculum prioritises reliability, evaluation, safety, and real-world deployment over mere experimentation.

This instructor-led, live training in Évora (online or onsite) provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

By the end of this training, participants will be able to:

• Apply core statistical methods to pattern recognition.
• Use key models like neural networks and kernel methods for data analysis.
• Implement advanced techniques for complex problem-solving.
• Improve prediction accuracy by combining different models.

This hands-on, instructor-led training serves as a natural progression from the Python for Data Analysis course.

Participants are introduced to the fundamental concepts of Machine Learning and explore how these can be directly applied to data analysis tasks such as prediction, classification, and segmentation.

The curriculum emphasizes practical understanding, utilizing familiar tools like Python, Pandas, and Jupyter Notebook, without necessitating an advanced background in mathematics.

This instructor-led, live training in Évora (online or onsite) is aimed at intermediate-level data analysts, developers, or aspiring data scientists who wish to apply machine learning techniques in Python to extract insights, make predictions, and automate data-driven decisions.

By the end of this course, participants will be able to:

• Understand and differentiate key machine learning paradigms.
• Explore data preprocessing techniques and model evaluation metrics.
• Apply machine learning algorithms to solve real-world data problems.
• Use Python libraries and Jupyter notebooks for hands-on development.
• Build models for prediction, classification, recommendation, and clustering.