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Kaminia

P.E.O., Patron Pyrgos 608 B, Kaminia , Greece, 25002

Strategically located at 608 B Patron Pyrgos Street in the Kaminia district of Athens, Greece, Kaminia P.E.O. occupies a prestigious position within a key commercial and residential area. This prime location places the company near major thoroughfares, offering excellent accessibility to the heart of Athens' business districts and vibrant neighborhoods. Its position on Patron Pyrgos provides convenient connections to essential services, shopping centers, dining options, and transportation networks serving the Kaminia area and beyond. Kaminia P.E.O. benefits from its proximity to other businesses and the dynamic energy of this important Athenian suburb. From this strategic base in Kaminia, the company is well-positioned to effectively serve its clients and engage within the local community. The location represents a significant asset in the ongoing development of the Kaminia area.

Explore Our Courses

Large Language Models (LLMs) and Reinforcement Learning (RL)

 21 Hours

This instructor-led, live training in Patras (online or onsite) is designed for intermediate-level data scientists who wish to acquire a comprehensive understanding and practical skills in both Large Language Models (LLMs) and Reinforcement Learning (RL).

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

  • Understand the components and functionality of transformer models.
  • Optimize and fine-tune LLMs for specific tasks and applications.
  • Understand the core principles and methodologies of reinforcement learning.
  • Learn how reinforcement learning techniques can enhance the performance of LLMs.
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Fine-Tuning with Reinforcement Learning from Human Feedback (RLHF)

 14 Hours

This instructor-led, live training held in Patras (online or onsite) is tailored for advanced-level machine learning engineers and AI researchers who aim to apply RLHF to fine-tune large AI models for enhanced performance, safety, and alignment.

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

  • Understand the theoretical foundations of RLHF and its significance in modern AI development.
  • Implement reward models based on human feedback to guide reinforcement learning processes.
  • Fine-tune large language models using RLHF techniques to align outputs with human preferences.
  • Apply best practices for scaling RLHF workflows for production-grade AI systems.
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Reinforcement Learning with Google Colab

 28 Hours

This instructor-led, live training in Patras (online or onsite) targets advanced-level professionals who aim to deepen their understanding of reinforcement learning and its practical applications in AI development using Google Colab.

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

  • Comprehend the fundamental principles underpinning reinforcement learning algorithms.
  • Construct reinforcement learning models utilising TensorFlow and OpenAI Gym.
  • Create intelligent agents that acquire knowledge through trial and error.
  • Enhance agent performance by applying advanced methodologies such as Q-learning and deep Q-networks (DQNs).
  • Train agents within simulated environments provided by OpenAI Gym.
  • Deploy reinforcement learning models for real-world utilisation.
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Deep Reinforcement Learning with Python

 21 Hours

Deep Reinforcement Learning (DRL) merges reinforcement learning principles with deep learning architectures, empowering agents to make decisions by interacting with their environments. This technology drives numerous modern AI innovations, including autonomous vehicles, robotic control systems, algorithmic trading, and adaptive recommendation engines. DRL enables artificial agents to acquire strategies, refine policies, and execute autonomous decisions through trial-and-error processes guided by reward mechanisms.

This instructor-led live training (available online or onsite) targets intermediate-level developers and data scientists eager to master and apply Deep Reinforcement Learning techniques. Participants will learn to construct intelligent agents capable of making autonomous decisions within complex environments.

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

  • Comprehend the theoretical foundations and mathematical underpinnings of Reinforcement Learning.
  • Implement core RL algorithms, such as Q-Learning, Policy Gradients, and Actor-Critic methods.
  • Construct and train Deep Reinforcement Learning agents leveraging TensorFlow or PyTorch.
  • Deploy DRL solutions in practical applications, including gaming, robotics, and decision optimization.
  • Troubleshoot, visualize, and enhance training performance using contemporary tools.

Format of the Course

  • Interactive lectures accompanied by guided discussions.
  • Practical exercises and real-world implementations.
  • Live coding demonstrations and project-based applications.

Course Customization Options

  • To request a customized version of this course (for instance, substituting TensorFlow with PyTorch), please contact us to arrange.
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Fundamentals of Reinforcement Learning

 21 Hours

This instructor-led live training in Patras (online or onsite) is designed for data scientists seeking to go beyond traditional machine learning approaches. The course focuses on teaching computer programs to solve problems without relying on labeled data or big datasets.

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

  • Install and apply the libraries and programming language needed to implement Reinforcement Learning.
  • Create a software agent that is capable of learning through feedback instead of through supervised learning.
  • Program an agent to solve problems where decision making is sequential and finite.
  • Apply knowledge to design software that can learn in a way similar to how humans learn.
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Upcoming Courses

Fundamentals of Reinforcement Learning

2026-06-29 09:30
21 hours
Kaminia
911 EUR (Online)
1511 EUR (Classroom)

Fine-Tuning with Reinforcement Learning from Human Feedback (RLHF)

2026-07-13 09:30
14 hours
Kaminia
608 EUR (Online)
1008 EUR (Classroom)

Fine-Tuning with Reinforcement Learning from Human Feedback (RLHF)

2026-07-27 09:30
14 hours
Kaminia
608 EUR (Online)
1008 EUR (Classroom)

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