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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

Explainability in Deep Learning: Demystifying Black-Box Models

 21 Hours

This instructor-led, live training in Tashkent (available online or on-site) is designed for advanced-level professionals seeking to explore state-of-the-art XAI techniques for deep learning models, with an emphasis on building interpretable AI systems.

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

  • Understand the challenges associated with explainability in deep learning.
  • Implement advanced XAI techniques for neural networks.
  • Interpret decisions made by deep learning models.
  • Evaluate the trade-offs between model performance and transparency.
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Applied AI from Scratch in Python

 28 Hours
This hands-on training in Tashkent enables programmers to develop AI models from the ground up using Python. You will gain proficiency in supervised learning, neural networks, and unsupervised techniques by leveraging scikit-learn and Apache Spark. The course emphasizes practical Jupyter exercises aimed at solving real-world issues.
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Deep Reinforcement Learning with Python

 21 Hours
This instructor-led training in Tashkent covers the theoretical foundations and practical implementation of Deep Reinforcement Learning using Python. Participants will build and train DRL agents with TensorFlow or PyTorch, applying key algorithms like DQN and PPO to solve complex real-world problems.
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Artificial Intelligence (AI) Overview

 7 Hours
An introductory course on Tashkent that examines AI fundamentals, ranging from intelligent agents to machine learning. It empowers executives and architects to evaluate emerging AI trends, implement practical solutions, and boost business agility through automated strategies.
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Artificial Intelligence (AI) in Automotive

 14 Hours
Discover how Machine Learning and Deep Learning are revolutionizing the automotive industry. This Tashkent course explores fundamental concepts, spanning from elementary automation to independent decision-making, while covering neural networks and providing practical TensorFlow examples for real-world implementation.
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Artificial Neural Networks, Machine Learning, Deep Thinking

 21 Hours
This intensive three-day program in Tashkent provides a comprehensive exploration of the theoretical principles and practical applications of Artificial Neural Networks, Machine Learning, and Deep Learning. Participants will examine diverse network architectures, intricate learning mechanisms, and essential mathematical foundations, progressing systematically from basic perceptrons to sophisticated deep learning techniques.
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Pattern Recognition

 21 Hours

This instructor-led, live training in Tashkent (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.
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Understanding Deep Neural Networks

 35 Hours
This 35-hour course in Tashkent covers the fundamentals of deep neural networks, including CNNs, RNNs, and generative models like GANs. Participants gain hands-on experience with Theano and TensorFlow, learning to build, train, and deploy production-grade deep learning models for real-world applications.
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Testimonials (3)

Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.

Matthew Gay - Tarsus Pharmaceuticals

Course - Artificial Intelligence (AI) Overview

Working from first principles in a focused way, and moving to applying case studies within the same day

Maggie Webb - Department of Jobs, Regions, and Precincts

Course - Artificial Neural Networks, Machine Learning, Deep Thinking

That it was applying real company data. Trainer had a very good approach by making trainees participate and compete

Jimena Esquivel - Zaklad Uslugowy Hakoman Andrzej Cybulski

Course - Applied AI from Scratch in Python

Upcoming Courses

Explainability in Deep Learning: Demystifying Black-Box Models

2026-11-19 09:30
21 hours
Mirobod
33,896,260 UZS (Online)
36,896,260 UZS (Classroom)

Explainability in Deep Learning: Demystifying Black-Box Models

2026-12-03 09:30
21 hours
Mirobod
33,896,260 UZS (Online)
36,896,260 UZS (Classroom)

Applied AI from Scratch in Python

2026-12-17 09:30
28 hours
Mirobod
45,195,013 UZS (Online)
49,195,013 UZS (Classroom)

Applied AI from Scratch in Python

2026-12-31 09:30
28 hours
Mirobod
45,195,013 UZS (Online)
49,195,013 UZS (Classroom)

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