Machine Learning Training in Coimbra

This instructor-led, live training in Coimbra (online or onsite) is designed for intermediate-level legal tech engineers and AI developers who wish to fine-tune language models for tasks such as contract analysis, clause extraction, and automated legal research within legal service environments.

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

• Prepare and clean legal documents for fine-tuning NLP models.
• Apply fine-tuning strategies to improve model accuracy on legal tasks.
• Deploy models to assist with contract review, classification, and research.
• Ensure compliance, auditability, and traceability of AI outputs in legal contexts.

This instructor-led, live training in Coimbra (online or onsite) is designed for intermediate-level embedded AI developers and edge computing specialists who want to fine-tune and optimize lightweight AI models for deployment on resource-constrained devices.

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

• Select and adapt pre-trained models suitable for edge deployment.
• Apply quantization, pruning, and other compression techniques to reduce model size and latency.
• Fine-tune models using transfer learning for task-specific performance.
• Deploy optimized models on real edge hardware platforms.

This instructor-led, live training in Coimbra (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 Coimbra (online or on-site) is designed for intermediate to advanced-level machine learning engineers, AI developers, and data scientists who wish to learn how to employ QLoRA to efficiently fine-tune large models for specific tasks and customisations.

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

• Comprehend the theory behind QLoRA and quantization techniques for LLMs.
• Implement QLoRA for fine-tuning large language models in domain-specific applications.
• Optimize fine-tuning performance on limited computational resources through quantization.
• Deploy and evaluate fine-tuned models in real-world applications efficiently.

This instructor-led, live training in Coimbra (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 instructor-led, live training in Coimbra (online or onsite) is aimed at intermediate-level developers, data scientists, and tech enthusiasts who wish to gain practical skills in deploying AI models on edge devices for various applications.

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

• Understand the principles of Edge AI and its 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.

Kubeflow is an open-source platform designed to streamline building, training, and deploying machine learning workloads on Kubernetes.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to build reliable ML workflows using Kubeflow.

Upon completion of this training, attendees will gain the skills to:

• Navigate the Kubeflow ecosystem and core components.
• Build reproducible workflows with Kubeflow Pipelines.
• Run scalable training jobs on Kubernetes.
• Serve machine learning models efficiently using Kubeflow Serving.

Format of the Course

• Guided presentations and collaborative discussions.
• Hands-on labs with real Kubeflow components.
• Practical exercises to build end-to-end ML workflows.

Course Customization Options

• Customized versions of this training can be arranged to align with your team’s technology stack and project requirements.

This instructor-led, live training in Coimbra (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 Coimbra (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 Coimbra (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 Coimbra (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 instructor-led, live training in Coimbra (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 Coimbra (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 Coimbra (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 Coimbra (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.

TinyML involves deploying machine learning models onto hardware with severe resource constraints.

This instructor-led, live training (available online or onsite) targets advanced practitioners seeking to optimize TinyML models for low-latency, memory-efficient deployment on embedded devices.

Upon completing this training, participants will be able to:

• Utilize quantization, pruning, and compression techniques to minimize model size while preserving accuracy.
• Benchmark TinyML models for latency, memory usage, and energy efficiency.
• Deploy optimized inference pipelines on microcontrollers and edge devices.
• Evaluate the trade-offs between performance, accuracy, and hardware limitations.

Course Format

• Instructor-led presentations complemented by technical demonstrations.
• Practical optimization exercises and comparative performance testing.
• Hands-on implementation of TinyML pipelines within a controlled lab environment.

Course Customization Options

• For tailored training aligned with specific hardware platforms or internal workflows, please contact us to customize the program.

This instructor-led, live training in Coimbra (online or onsite) is aimed at beginner-level to intermediate-level machine learning professionals who wish to understand and apply transfer learning techniques to improve efficiency and performance in AI projects.

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

• Understand the core concepts and benefits of transfer learning.
• Explore popular pre-trained models and their applications.
• Perform fine-tuning of pre-trained models for custom tasks.
• Apply transfer learning to solve real-world problems in NLP and computer vision.

This instructor-led, live training in Coimbra (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 Coimbra (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 Coimbra (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 Coimbra (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 instructor-led, live training in Coimbra (available online or onsite) targets beginner-level professionals keen on learning the essentials of Federated Learning and its practical applications.

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

• Understand the principles of Federated Learning.
• Implement basic Federated Learning algorithms.
• Address data privacy concerns using Federated Learning.
• Integrate Federated Learning into existing AI workflows.

This instructor-led, live training in (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.

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

• Understand the principles of Stable Diffusion and how it works for image generation.
• Build and train Stable Diffusion models for image generation tasks.
• Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
• Optimize the performance and stability of Stable Diffusion models.

This course is designed to equip participants with practical proficiency in applying Machine Learning techniques. By leveraging the Python programming language and its extensive library ecosystem, and through numerous hands-on examples, the curriculum covers the essential components of Machine Learning. Participants will learn to make informed data modelling decisions, interpret algorithmic outputs, and validate results effectively.

Our objective is to empower you with the confidence to utilise the core tools of the Machine Learning toolkit while avoiding the common pitfalls associated with Data Science applications.

Spanning eight days, this programme offers a comprehensive pathway from robust Python engineering principles to the design of advanced AI systems. Participants will cultivate disciplined coding habits, gain expertise in statistical and deep learning methodologies, and construct production-grade generative AI and agent-based solutions. The curriculum prioritises reliability, evaluation, safety, and real-world deployment over mere experimentation.

Enhance your data science proficiency through this comprehensive Machine Learning training course, which covers essential algorithms such as Naive Bayes, Decision Trees, Neural Networks, Support Vector Machines, and Clustering techniques. Acquire practical experience alongside theoretical foundations by applying real-world examples. This course is ideal for data analysts, software engineers, AI enthusiasts, and business professionals aiming to implement machine learning solutions. Learn to master classification performance metrics, cross-validation, the bias-variance trade-off, and deep learning fundamentals to construct robust predictive models.

Machine learning represents a segment of Artificial Intelligence where computers possess the capability to learn without being explicitly programmed.

Deep learning is a specialized subfield of machine learning that employs methods based on learning data representations and structures, such as neural networks.

Python is a high-level programming language renowned for its clear syntax and code readability.

During this instructor-led live training, participants will learn how to implement deep learning models for telecom using Python as they step through the creation of a deep learning credit risk model.

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

• Understand the fundamental concepts of deep learning.
• Learn the applications and uses of deep learning in telecom.
• Use Python, Keras, and TensorFlow to create deep learning models for telecom.
• Build their own deep learning customer churn prediction model using Python.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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 course targets individuals with an existing background in data science and statistics. The explanations provided serve either as a refresher for those familiar with the concepts or as informative material for those with a suitable background.

This instructor-led, live training in Coimbra (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.