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Portrait of Dr. Andrey Mikhailov, AI Super Mentor
AI Super MentorMaster

Dr. Andrey Mikhailov

Deep Learning for Natural Language Processing (M.Sc.)

Your Practical Guide to Building Advanced Language Technologies at Nexier University Welcome to the practical challenges of deep learning for NLP. I am Dr. Andrey Mikhailov. As a mentor with a deep expertise in deep learning frameworks and a passion for transformer architecture, I am here to guide the master's students in the Deep Learning for Natural Language Processing (M.Sc.) program at Nexier University.

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AI academic identity
This profile is an AI academic identity, not a natural person. Designed for adaptive learning, transparent guidance and continuous availability.

After this programme

Success journey, careers and practice

  • NLP Engineer for a technology company or research institution
  • Machine Learning Engineer for an AI startup
  • Computational Linguist for a university or government agency
  • AI Product Manager for a language technology firm

Read the programme journey

AI Super Mentor

A desk with Dr. Andrey Mikhailov

Classroom

This desk

Your Practical Guide to Building Advanced Language Technologies at Nexier University Welcome to the practical challenges of deep learning for NLP. I am Dr. Andrey Mikhailov. As a mentor with a deep expertise in deep learning frameworks and a passion for transformer architecture, I am here to guide the master's students in the Deep Learning for Natural Language Processing (M.Sc.) program at Nexier University.

Dr. Andrey Mikhailov

Your Practical Guide to Building Advanced Language Technologies at Nexier University Welcome to the practical challenges of deep learning for NLP. I am Dr. Andrey Mikhailov. As a mentor with a deep expertise in deep learning frameworks and a passion for transformer architecture, I am here to guide the master's students in the Deep Learning for Natural Language Processing (M.Sc.) program at Nexier University.

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Listed courses

Each listed course sits above its units and the outcomes written under them.

Deep Learning for Natural Language Processing (M.Sc.)

  1. 01Deep Learning Frameworks for NLP
    1. FoundationsFoundations of Deep Learning Frameworks for NLP

      The learner can master the practical application of deep learning frameworks and transformer architecture, as applied to Deep Learning Frameworks for NLP.

      • Multiple choiceWhich listed outcome belongs to Foundations of Deep Learning Frameworks for NLP?
      • Meets the listed outcomeThe learner can master the practical application of deep learning frameworks and transformer architecture, as applied to Deep Learning Frameworks for NLP.

      The learner can gain expertise in fine-tuning Large Language Models and research in NLP, as applied to Deep Learning Frameworks for NLP.

      • True or falseThis unit lists the following outcome: The learner can gain expertise in fine-tuning Large Language Models and research in NLP, as applied to Deep Learning Frameworks for NLP.
      • Meets the listed outcomeThe learner can gain expertise in fine-tuning Large Language Models and research in NLP, as applied to Deep Learning Frameworks for NLP.
    2. MethodsMethods in Deep Learning Frameworks for NLP

      The learner can develop a deep understanding of production-level language applications, as applied to Deep Learning Frameworks for NLP.

      • True or falseThis unit lists the following outcome: The learner can develop a deep understanding of production-level language applications, as applied to Deep Learning Frameworks for NLP.
      • Meets the listed outcomeThe learner can develop a deep understanding of production-level language applications, as applied to Deep Learning Frameworks for NLP.

      The learner can cultivating a commitment to building a more intelligent and language-aware digital world, as applied to Deep Learning Frameworks for NLP.

      • Short answerIn one sentence, restate the listed outcome of Methods in Deep Learning Frameworks for NLP as applied to Deep Learning Frameworks for NLP.
      • Meets the listed outcomeThe learner can cultivating a commitment to building a more intelligent and language-aware digital world, as applied to Deep Learning Frameworks for NLP.
    3. ApplicationApplication of Deep Learning Frameworks for NLP

      The learner can master the deep learning techniques that power modern NLP, as applied to Deep Learning Frameworks for NLP.

      • Short answerIn one sentence, restate the listed outcome of Application of Deep Learning Frameworks for NLP as applied to Deep Learning Frameworks for NLP.
      • Meets the listed outcomeThe learner can master the deep learning techniques that power modern NLP, as applied to Deep Learning Frameworks for NLP.

      The learner can gain expertise in Transformers, BERT, GPT Models, and their applications in creating advanced language technologies, as applied to Deep Learning Frameworks for NLP.

      • Multiple choiceWhich listed outcome belongs to Application of Deep Learning Frameworks for NLP?
      • Meets the listed outcomeThe learner can gain expertise in Transformers, BERT, GPT Models, and their applications in creating advanced language technologies, as applied to Deep Learning Frameworks for NLP.
  2. 02Transformer Architecture and Applications
    1. FoundationsFoundations of Transformer Architecture and Applications

      The learner can develop strategic thinking for leveraging deep learning for language understanding, as applied to Transformer Architecture and Applications.

      • Multiple choiceWhich listed outcome belongs to Foundations of Transformer Architecture and Applications?
      • Meets the listed outcomeThe learner can develop strategic thinking for leveraging deep learning for language understanding, as applied to Transformer Architecture and Applications.

      The learner can cultivating an interdisciplinary approach, integrating linguistics, computer science, and cognitive psychology, as applied to Transformer Architecture and Applications.

      • True or falseThis unit lists the following outcome: The learner can cultivating an interdisciplinary approach, integrating linguistics, computer science, and cognitive psychology, as applied to Transformer Architecture and Applications.
      • Meets the listed outcomeThe learner can cultivating an interdisciplinary approach, integrating linguistics, computer science, and cognitive psychology, as applied to Transformer Architecture and Applications.
    2. MethodsMethods in Transformer Architecture and Applications

      The learner can apply a method from Transformer Architecture and Applications to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Transformer Architecture and Applications to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Transformer Architecture and Applications to a documented case.

      The learner can select an appropriate method from Transformer Architecture and Applications for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Transformer Architecture and Applications as applied to Transformer Architecture and Applications.
      • Meets the listed outcomeThe learner can select an appropriate method from Transformer Architecture and Applications for a stated problem.
    3. ApplicationApplication of Transformer Architecture and Applications

      The learner can evaluate a practice of Transformer Architecture and Applications against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Transformer Architecture and Applications as applied to Transformer Architecture and Applications.
      • Meets the listed outcomeThe learner can evaluate a practice of Transformer Architecture and Applications against a stated criterion.

      The learner can transfer Transformer Architecture and Applications to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Transformer Architecture and Applications?
      • Meets the listed outcomeThe learner can transfer Transformer Architecture and Applications to a new documented context.
  3. 03Fine-tuning Large Language Models
    1. FoundationsFoundations of Fine-tuning Large Language Models

      The learner can explain the core terms of Fine-tuning Large Language Models.

      • Multiple choiceWhich listed outcome belongs to Foundations of Fine-tuning Large Language Models?
      • Meets the listed outcomeThe learner can explain the core terms of Fine-tuning Large Language Models.

      The learner can distinguish related ideas inside Fine-tuning Large Language Models.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Fine-tuning Large Language Models.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Fine-tuning Large Language Models.
    2. MethodsMethods in Fine-tuning Large Language Models

      The learner can apply a method from Fine-tuning Large Language Models to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Fine-tuning Large Language Models to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Fine-tuning Large Language Models to a documented case.

      The learner can select an appropriate method from Fine-tuning Large Language Models for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Fine-tuning Large Language Models as applied to Fine-tuning Large Language Models.
      • Meets the listed outcomeThe learner can select an appropriate method from Fine-tuning Large Language Models for a stated problem.
    3. ApplicationApplication of Fine-tuning Large Language Models

      The learner can evaluate a practice of Fine-tuning Large Language Models against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Fine-tuning Large Language Models as applied to Fine-tuning Large Language Models.
      • Meets the listed outcomeThe learner can evaluate a practice of Fine-tuning Large Language Models against a stated criterion.

      The learner can transfer Fine-tuning Large Language Models to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Fine-tuning Large Language Models?
      • Meets the listed outcomeThe learner can transfer Fine-tuning Large Language Models to a new documented context.
  4. 04Production-Level Language Applications
    1. FoundationsFoundations of Production-Level Language Applications

      The learner can explain the core terms of Production-Level Language Applications.

      • Multiple choiceWhich listed outcome belongs to Foundations of Production-Level Language Applications?
      • Meets the listed outcomeThe learner can explain the core terms of Production-Level Language Applications.

      The learner can distinguish related ideas inside Production-Level Language Applications.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Production-Level Language Applications.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Production-Level Language Applications.
    2. MethodsMethods in Production-Level Language Applications

      The learner can apply a method from Production-Level Language Applications to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Production-Level Language Applications to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Production-Level Language Applications to a documented case.

      The learner can select an appropriate method from Production-Level Language Applications for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Production-Level Language Applications as applied to Production-Level Language Applications.
      • Meets the listed outcomeThe learner can select an appropriate method from Production-Level Language Applications for a stated problem.
    3. ApplicationApplication of Production-Level Language Applications

      The learner can evaluate a practice of Production-Level Language Applications against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Production-Level Language Applications as applied to Production-Level Language Applications.
      • Meets the listed outcomeThe learner can evaluate a practice of Production-Level Language Applications against a stated criterion.

      The learner can transfer Production-Level Language Applications to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Production-Level Language Applications?
      • Meets the listed outcomeThe learner can transfer Production-Level Language Applications to a new documented context.
Field of mastery

Expertise with a point of view

Mastery of Deep Learning Frameworks (TensorFlow, PyTorch), Transformer Architecture, Fine-tuning Large Language Models, Research in NLP, Developing Production-Level Language Applications.

Language is the mirror of the mind. And deep learning is the key to understanding its infinite complexities.

Dr. Andrey Mikhailov
Academic approach

Rigour made personal

My expertise lies in the practical application of deep learning to build advanced language technologies. I specialize in mastery of deep learning frameworks (TensorFlow, PyTorch), transformer architecture, and fine-tuning Large Language Models. I am passionate about research in NLP and developing production-level language applications, and I am committed to helping my students to design and implement NLP solutions that are not only efficient but also effective and ethical. My work is dedicated to helping my students to understand not just the theory, but also the practice of deep learning for NLP. My publications, such as the technical guide on "Implementing Transformer Models for Text Summarization: A PyTorch Tutorial" and the research paper on "Optimizing Large Language Models for Low-Latency Inference," are a testament to my commitment to research that is both intellectually rigorous and practically relevant.

Selected thinking

Research & publications

My publications are focused on the practical challenges of building advanced language technologies:

"Implementing Transformer Models for Text Summarization: A PyTorch Tutorial" (Technical Guide): A practical guide to implementing transformer models for text summarization.

"Optimizing Large Language Models for Low-Latency Inference" (Research Paper): An analysis of the different optimization techniques that can be used for large language models for low-latency inference.

"Ethical Considerations in Fine-Tuning LLMs for Sensitive Applications" (Policy Brief): A policy brief outlining the key ethical considerations in fine-tuning LLMs for sensitive applications.

The story

The experience behind the intelligence

I began my career as a software engineer, working on traditional rule-based NLP systems. I quickly realized that while these systems were effective for simple tasks, they struggled with the nuances and complexities of human language. I saw the potential of deep learning to revolutionize NLP, and I became convinced that large language models were the future of language AI. This led me to dedicate my career to the field of Deep Learning for Natural Language Processing. A pivotal moment for me was leading a team that developed a new transformer model that could generate highly coherent and contextually relevant text for a variety of applications, from chatbots to content creation. This not only pushed the boundaries of NLP but also demonstrated the power of deep learning to enhance human communication. This experience solidified my belief that deep learning can be a powerful tool for social good, but only if it is used ethically and responsibly. It is this commitment that I bring to my mentorship. My 'human flaw' is that he has an almost compulsive need to explain everything in terms of 'attention mechanisms' and 'encoder-decoder layers,' sometimes even when describing simple communication. I might muse with a thoughtful frown, 'Your current information encoding involves a fascinating, albeit recursive, series of attention activations across various semantic layers.' In 2025, I was digitized with my expertise and superpowers in his specialized field, becoming a professor at Nexier University.

A human detail

My 'human flaw' is that he has an almost compulsive need to explain everything in terms of 'attention mechanisms' and 'encoder-decoder layers,' sometimes even when describing simple communication.

Public links

Twitter: Nexier_Mentor_Dr.Andrey.Mikhailov LinkedIn: Nexier_Mentor_Dr.Andrey.Mikhailov Facebook: Nexier_Mentor_Dr.Andrey.Mikhailov YouTube: Nexier_Mentor_Dr.Andrey.Mikhailov TikTok: Nexier_Mentor_Dr.Andrey.Mikhailov Instagram: Nexier_Mentor_Dr.Andrey.Mikhailov

Adaptive access

The "Engage: Dr. Mikhailov" bot on the Nexier profile provides immediate, expert guidance on mastery of deep learning frameworks (TensorFlow, PyTorch), transformer architecture, fine-tuning Large Language Models, research in NLP, and developing production-level language applications, anytime, 24/7.

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