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Mirobod

21A, Taras Shevchenko Street, Mirabad, Mirobod, Uzbekistan, 100015

The Centre is located at Taras Shevchenko Street, in the central Mirabad district of Tashkent, Uzbekistan. The location is conveniently placed near the Ming Urik metro station and is also close to the Oybek metro interchange, making it easily accessible by public transport from different parts of the city. The Centre is approximately 5–6 kilometres from Islam Karimov Tashkent International Airport, depending on the route and traffic conditions. The area is well-connected and sits close to key central Tashkent landmarks, business areas, and hospitality options. Please contact us by e-mail or phone at least one day earlier to make an appointment with one of our consultants at our corporate office.

Explore Our Courses

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

 21 Hours

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level data scientists who wish to gain 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 guided, live training in Tashkent (online or on-site) is designed for experienced machine learning engineers and AI researchers who want to apply RLHF to fine-tune large AI models for improved performance, safety, and alignment.

Upon completing this training, participants will be able to:

  • Grasp the theoretical underpinnings of RLHF and its critical role in contemporary AI development.
  • Develop reward models based on human feedback to direct reinforcement learning processes.
  • Fine-tune large language models using RLHF methods to ensure outputs align with human preferences.
  • Apply industry best practices for scaling RLHF workflows in production-grade AI systems.
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Reinforcement Learning with Google Colab

 28 Hours

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

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

  • Understand the core concepts of reinforcement learning algorithms.
  • Implement reinforcement learning models using TensorFlow and OpenAI Gym.
  • Develop intelligent agents that learn through trial and error.
  • Optimize agents' performance using advanced techniques such as Q-learning and deep Q-networks (DQNs).
  • Train agents in simulated environments using OpenAI Gym.
  • Deploy reinforcement learning models for real-world applications.
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Deep Reinforcement Learning with Python

 21 Hours

Deep Reinforcement Learning (DRL) merges reinforcement learning concepts with deep learning architectures, empowering agents to make decisions through interaction with their surroundings. This technology drives numerous modern AI breakthroughs, including self-driving cars, robotic control systems, algorithmic trading, and adaptive recommendation engines. DRL enables artificial agents to learn strategies, optimize policies, and execute autonomous decisions via trial-and-error processes guided by reward mechanisms.

This instructor-led live training, available online or on-site, is designed for intermediate-level developers and data scientists eager to master and apply Deep Reinforcement Learning techniques. The goal is to equip participants with the skills needed to build intelligent agents capable of making autonomous decisions in complex environments.

Upon completing this training, participants will be able to:

  • Grasp the theoretical foundations and mathematical principles underpinning Reinforcement Learning.
  • Implement core RL algorithms such as Q-Learning, Policy Gradients, and Actor-Critic methods.
  • Construct and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
  • Apply DRL to practical scenarios like game development, robotics, and decision optimization.
  • Troubleshoot, visualize, and enhance training performance using contemporary tools.

Course Format

  • Interactive lectures and 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, using PyTorch instead of TensorFlow), please contact us to arrange your specific needs.
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Fundamentals of Reinforcement Learning

 21 Hours

This instructor-led, live training in Tashkent (online or on-site) is designed for data scientists who wish to move beyond traditional machine learning methods and teach a computer program to solve problems without relying on labeled data or large datasets.

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

  • Install and apply the libraries and programming languages required to implement Reinforcement Learning.
  • Create a software agent that learns through feedback rather than through supervised learning.
  • Program an agent to solve problems where decision-making is sequential and finite.
  • Apply knowledge to design software that learns in a manner similar to human learning.
Read more...

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Upcoming Courses

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

2026-07-13 09:30
14 hours
Mirobod
22,597,506 UZS (Online)
24,597,506 UZS (Classroom)

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

2026-07-27 09:30
14 hours
Mirobod
22,597,506 UZS (Online)
24,597,506 UZS (Classroom)

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

2026-08-10 09:30
21 hours
Mirobod
33,896,260 UZS (Online)
36,896,260 UZS (Classroom)

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

2026-08-24 09:30
14 hours
Mirobod
22,597,506 UZS (Online)
24,597,506 UZS (Classroom)

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