Hotel Central Parque

83, Av. Visc. de Barreiros 83, 4470-151 Maia, Maia, Portugal, 4470-151 Maia

Hotel Central Parque **** is located in the center of the city of Maia, due to its proximity to Francisco Sá Carneiro Airport, Zona Industrial da Maia, Exponor Matosinhos, the metro station and the main highways, it becomes the place ideal for your stay.

Subcategories (10)

Category for Deep Learning

Deep Learning
Category for Fine-Tuning

Fine-Tuning
Category for Federated Learning

Federated Learning
Category for Reinforcement Learning

Reinforcement Learning
Category for TinyML

TinyML
Category for Explainable AI (XAI)

Explainable AI (XAI)
Category for MLOps

MLOps
Category for Supervised Learning

Supervised Learning
Category for Unsupervised Learning

Unsupervised Learning
Category for Ensemble Learning

Ensemble Learning

Explore Our Courses

Introduction to Pre-trained Models

 14 Hours
This instructor-led, live training in Maia (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.
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Advanced Machine Learning Models with Google Colab

 21 Hours
This instructor-led, live training in Maia (online or onsite) is aimed at advanced-level professionals who wish to enhance their knowledge of machine learning models, improve their skills in hyperparameter tuning, and learn how to deploy models effectively using Google Colab. By the end of this training, participants will be able to:
  • Implement advanced machine learning models using popular frameworks like Scikit-learn and TensorFlow.
  • Optimize model performance through hyperparameter tuning.
  • Deploy machine learning models in real-world applications using Google Colab.
  • Collaborate and manage large-scale machine learning projects in Google Colab.
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Optimizing AI Models for Edge Devices

 14 Hours
This instructor-led, live training in Maia (online or onsite) is aimed at intermediate-level AI developers, machine learning engineers, and system architects who wish to optimize AI models for edge deployment. By the end of this training, participants will be able to:
  • Understand the challenges and requirements of deploying AI models on edge devices.
  • Apply model compression techniques to reduce the size and complexity of AI models.
  • Utilize quantization methods to enhance model efficiency on edge hardware.
  • Implement pruning and other optimization techniques to improve model performance.
  • Deploy optimized AI models on various edge devices.
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Building AI Solutions on the Edge

 14 Hours
This instructor-led, live training in Maia (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.
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AI-Powered Autonomous Systems

 21 Hours
This instructor-led, live training in Maia (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.
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AI-Enhanced Yield Management in Semiconductor Production

 14 Hours
This instructor-led, live training in Maia (online or onsite) is aimed at 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.
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Machine Learning for Business and AI Applications

 21 Hours
This instructor-led, live training in Maia (online or onsite) is aimed at 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.By the end of this training, participants will be able to:
  • Understand how machine learning fits within AI and business strategy.
  • Apply supervised and unsupervised learning techniques to structured business problems.
  • Preprocess and transform data for modeling.
  • Use neural networks for classification and prediction tasks.
  • Perform sales forecasting using statistical and ML-based methods.
  • Implement clustering and association rule mining for customer segmentation and pattern discovery.
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Advanced AI Techniques for Semiconductor Design Automation

 21 Hours
This instructor-led, live training in Maia (online or onsite) is aimed at advanced-level professionals who wish to apply cutting-edge AI techniques to semiconductor design automation, improving efficiency, accuracy, and innovation in chip design and verification. By the end of this training, participants will be able to:
  • Apply advanced AI techniques to optimize semiconductor design processes.
  • Integrate machine learning models into EDA tools for enhanced design verification.
  • Develop AI-driven solutions for complex design challenges in chip fabrication.
  • Leverage neural networks for improving the accuracy and speed of design automation.
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AI-Driven Process Optimization in Chip Fabrication

 14 Hours
This instructor-led, live training in Maia (online or onsite) is aimed at intermediate-level professionals who wish to understand and apply AI techniques for optimizing semiconductor fabrication processes. By the end of this training, participants will be able to:
  • Understand AI methodologies for process optimization in chip fabrication.
  • Implement AI models to enhance yield and reduce defects.
  • Analyze process data to identify key parameters for optimization.
  • Apply machine learning techniques to fine-tune semiconductor manufacturing processes.
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Apache Airflow for Data Science: Automating Machine Learning Pipelines

 21 Hours
This instructor-led, live training in Maia (online or onsite) is aimed at intermediate-level participants who wish to automate and manage machine learning workflows, including model training, validation, and deployment using Apache Airflow. By the end of this training, participants will be able to:
  • Set up Apache Airflow for machine learning workflow orchestration.
  • Automate data preprocessing, model training, and validation tasks.
  • Integrate Airflow with machine learning frameworks and tools.
  • Deploy machine learning models using automated pipelines.
  • Monitor and optimize machine learning workflows in production.
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Machine Learning and Predictive Analytics with Python

 28 Hours
This instructor-led, live training in Maia (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.
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AI-Powered Cybersecurity: Advanced Threat Detection & Response

 28 Hours
This instructor-led, live training in Maia (online or onsite) is aimed at intermediate-level to advanced-level cybersecurity professionals who wish to elevate 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 for specific cybersecurity challenges.
  • Develop automation workflows for threat response.
  • Secure AI-driven security tools against adversarial attacks.
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Machine Learning with Google Colab

 14 Hours
This instructor-led, live training in Maia (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.
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AI-Powered Cybersecurity: Threat Detection & Response

 21 Hours
This instructor-led, live training in Maia (online or onsite) is aimed at beginner-level cybersecurity professionals who wish to learn how to leverage AI for improved threat detection and response capabilities. By the end of this training, participants will be able to:
  • Understand AI applications in cybersecurity.
  • Implement AI algorithms for threat detection.
  • Automate incident response with AI tools.
  • Integrate AI into existing cybersecurity infrastructure.
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Machine Learning with Python – 2 Days

 14 Hours
The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
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Machine Learning with Python – 4 Days

 28 Hours
The aim of this course is to provide general proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
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Machine Learning

 21 Hours
This course will be a combination of theory and practical work with specific examples used throughout the event.
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Feature Engineering for Machine Learning

 14 Hours
This instructor-led, live training in Maia (online or onsite) is aimed at engineers who wish to apply feature engineering techniques to better process data and achieve obtain better machine learning models. By the end of this training, participants will be able to:
  • Set up an optimal development environment, including all needed Python packages.
  • Obtain important insights by analyzing the features of a data set.
  • Optimize machine learning models through adaptation of the raw data itself.
  • Clean and transform data sets in preparation for machine learning.
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Fundamentals of Artificial Intelligence (AI) and Machine Learning

 28 Hours
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In 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.
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Machine Learning Algorithms in Julia

 21 Hours
This course is for people that already have a background in data science and statistics. The explanations given are designed to either serve as a reminder to those that are already familiar with the concepts or inform those with a suitable background.
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Mathematica for Machine Learning

 14 Hours
This instructor-led, live training in Maia (online or onsite) is aimed at data scientists who wish to use machine learning in Mathematica for data analysis. By the end of this training, participants will be able to:
  • Build and train machine learning models.
  • Import and prepare data for machine learning.
  • Separate training data from test data.
  • Explore deep learning and neural network applications in data analysis.
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Machine Learning for Data Science with Python

 21 Hours
This instructor-led, live training in Maia (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.
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Machine Learning for Banking (with Python)

 21 Hours
Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications. In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects. Audience
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Machine Learning and Big Data

 7 Hours
This instructor-led, live training in Maia (online or onsite) is aimed at technical persons who wish to learn how to implement a machine learning strategy while maximizing the use of big data. By the end of this training, participants will:
  • Understand the evolution and trends for machine learning.
  • Know how machine learning is being used across different industries.
  • Become familiar with the tools, skills and services available to implement machine learning within an organization.
  • Understand how machine learning can be used to enhance data mining and analysis.
  • Learn what a data middle backend is, and how it is being used by businesses.
  • Understand the role that big data and intelligent applications are playing across industries.
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Machine Learning Concepts for Entrepreneurs and Managers

 21 Hours
This training course is for people that would like to apply Machine Learning in practical applications for their team.  The training will not dive into technicalities and revolve around basic concepts and business/operational applications of the same. Target Audience
  1. Investors and AI entrepreneurs
  2. Managers and Engineers whose company is venturing into AI space
  3. Business Analysts & Investors
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Machine Learning for Finance (with Python)

 21 Hours
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications. In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects. By the end of this training, participants will be able to:
  • Understand the fundamental concepts in machine learning
  • Learn the applications and uses of machine learning in finance
  • Develop their own algorithmic trading strategy using machine learning with Python
Audience
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
Read more...

Machine Learning Fundamentals with Scala and Apache Spark

 14 Hours
The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Scala programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
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Introduction to Machine Learning

 7 Hours
This training course is for people that would like to apply basic Machine Learning techniques in practical applications. Audience Data scientists and statisticians that have some familiarity with machine learning and know how to program R. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give a practical introduction to machine learning to participants interested in applying the methods at work Sector specific examples are used to make the training relevant to the audience.
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Machine Learning on iOS

 14 Hours
In this instructor-led, live training, participants will learn how to use the iOS Machine Learning (ML) technology stack as they step through the creation and deployment of an iOS mobile app. By the end of this training, participants will be able to:
  • Create a mobile app capable of image processing, text analysis and speech recognition
  • Access pre-trained ML models for integration into iOS apps
  • Create a custom ML model
  • Add Siri Voice support to iOS apps
  • Understand and use frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit
  • Use languages and tools such as Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder
Audience
  • Developers
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
Read more...

Machine Learning for Business and AI Systems

 14 Hours
This instructor-led, live training in Maia (online or onsite) is aimed at intermediate-level business and technical professionals who wish to apply machine learning techniques to solve real-world business challenges using practical case studies and hands-on tools.By the end of this training, participants will be able to:
  • Understand how machine learning fits into modern AI systems and business strategies.
  • Identify appropriate machine learning methods for different business problems.
  • Preprocess and transform business data for machine learning tasks.
  • Apply core machine learning techniques such as classification, regression, clustering, and time series forecasting.
  • Interpret and evaluate machine learning models in the context of business decision-making.
  • Gain hands-on experience through case studies and apply learned techniques to practical scenarios.
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Machine Learning for Robotics

 21 Hours
This course introduces machine learning methods in robotics applications. It is a broad overview of existing methods, motivations and main ideas in the context of pattern recognition. After a short theoretical background, participants will perform simple exercise using open source (usually R) or any other popular software.
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Testimonials (25)

Hunter is fabulous, very engaging, extremely knowledgeable and personable. Very well done.

Rick Johnson - Laramie County Community College

Course - Artificial Intelligence (AI) Overview

the VM is a nice idea

Vincent - REGNOLOGY ROMANIA S.R.L.

Course - Fundamentals of Artificial Intelligence (AI) and Machine Learning

I thought the trainer was very knowledgeable and answered questions with confidence to clarify understanding.

Jenna - TCMT

Course - Machine Learning with Python – 2 Days

The clarity with which it was presented

John McLemore - Motorola Solutions

Course - Deep Learning for Telecom (with Python)

the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose

Guillaume GAUTIER - OLEA MEDICAL

Course - MLflow

The way of transferring knowledge and the knowledge of the trainer.

Jakub Rekas - Bitcomp Sp. z o.o.

Course - Machine Learning on iOS

The explaination

Wei Yang Teo - Ministry of Defence, Singapore

Course - Machine Learning with Python – 4 Days

I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.

Guillaume Gautier - OLEA MEDICAL | Improved diagnosis for life TM

Course - Kubeflow

The enthusiasm to the topic. The examples he made an he explained it very well. Sympatic. A little to detailed for beginners. For managers, it could be more abstract in fewer days. But it was designed to fit and we had a good alignment in advance.

Benedikt Chiandetti - HDI Deutschland Bancassurance Kundenservice GmbH

Course - Machine Learning Concepts for Entrepreneurs and Managers

The trainer explained the content well and was engaging throughout. He stopped to ask questions and let us come to our own solutions in some practical sessions. He also tailored the course well for our needs.

Robert Baker

Course - Deep Learning with TensorFlow 2.0

Convolution filter

Francesco Ferrara

Course - Introduction to Machine Learning

Tomasz really know the information well and the course was well paced.

Raju Krishnamurthy - Google

Course - TensorFlow Extended (TFX)

Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject

Ali Kattan - TWPI

Course - Natural Language Processing with TensorFlow

The trainer was a professional in the subject field and related theory with application excellently

Fahad Malalla - Tatweer Petroleum

Course - Applied AI from Scratch in Python

It is showing many methods with pre prepared scripts- very nicely prepared materials & easy to traceback

Kamila Begej - GE Medical Systems Polska Sp. Zoo

Course - Machine Learning – Data science

I liked the lab exercises.

Marcell Lorant - L M ERICSSON LIMITED

Course - Machine Learning

In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.

Sacha Nandlall

Course - Python for Advanced Machine Learning

Very flexible.

Frank Ueltzhoffer

Course - Artificial Neural Networks, Machine Learning and Deep Thinking

Very updated approach or CPI (tensor flow, era, learn) to do machine learning.

Paul Lee

Course - TensorFlow for Image Recognition

I liked the new insights in deep machine learning.

Josip Arneric

Course - Neural Network in R

We have gotten a lot more insight in to the subject matter. Some nice discussion were made with some real subjects within our company.

Sebastiaan Holman

Course - Machine Learning and Deep Learning

The global overview of deep learning.

Bruno Charbonnier

Course - Advanced Deep Learning

The topic is very interesting.

Wojciech Baranowski

Course - Introduction to Deep Learning

Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.

Gudrun Bickelq

Course - Introduction to the use of neural networks

It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.

Jonathan Blease

Course - Artificial Neural Networks, Machine Learning, Deep Thinking

Provisional Upcoming Courses (Contact Us For More Information)

Machine Learning for Robotics

2025-06-23 09:30
21 hours
Hotel Central Parque

Advanced AI Techniques for Semiconductor Design Automation

2025-07-07 09:30
21 hours
Hotel Central Parque

Machine Learning Fundamentals with Scala and Apache Spark

2025-07-21 09:30
14 hours
Hotel Central Parque

Introduction to Pre-trained Models

2025-08-04 09:30
14 hours
Hotel Central Parque

Fundamentals of Artificial Intelligence (AI) and Machine Learning

2025-08-18 09:30
28 hours
Hotel Central Parque

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