Михаил

KOHUTENKO MYKHAILO
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SUMMARY
Junior Machine Learning Engineer with a mathematical foundation (BSc in Mathematics) and an ongoing MSc
in System Analysis. Experience in building Deep Learning (CNN, RNN, LSTM) and NLP architectures through
academic and certification projects. Proficient in Python, TensorFlow, and Scikit-learn. Background in IT
infrastructure analysis provides a structured approach to technical documentation and data processing.

SKILLS
Programming & Data Processing Deep Learning & NLP
Python (Core, OOP), NumPy, Pandas TensorFlow, CNN, RNN, LSTM, GRU, HuggingFace,
Transformers
Machine Learning
Neural Networks (Back Propagation), Genetic Tools & Environment
Algorithms, Regression, Classification, VS Code, Jupyter Notebooks, Git
Recommender Systems, MSE, scikit-learn, Decision
Trees Mathematical Foundations
Linear Algebra, Calculus, Statistics, Optimization
IT Infrastructure & Tools Methods
Server Hardware, Cisco Nexus, VMware, Git, Jupyter
Notebooks, Enterprise IT Solutions, Dell Server
Hardware, System Optimization

PROJECTS
Machine Learning Predictive Models & Recommender Systems
Stanford University Certification Project
Built and trained supervised models for prediction and binary classification using Python and Scikit-learn.
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Implemented Linear and Logistic Regression models utilizing gradient descent and L1/L2 regularization to
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prevent overfitting.
Developed a Recommender System applying collaborative filtering techniques.
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Deep Neural Networks & Image Classification
DeepLearning.AI Certification Project
Built and trained deep L-layer neural networks from scratch using Python and NumPy, applying
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vectorization and backpropagation.
Implemented Convolutional Neural Networks (CNN) in TensorFlow for image detection and recognition
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tasks.
Applied standard optimization algorithms for Deep Learning applications to improve variance and model
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performance.
Sequence Models & Natural Language Processing
DeepLearning.AI Certification Project
Implemented sentiment analysis and named entity recognition (NER) models using Recurrent Neural
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Networks (RNN), LSTMs, and GRUs.
Utilized HuggingFace tokenizers, transformer models, and word embeddings for text processing tasks.
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Applied encoder-decoder architectures for machine translation and text summarization.
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EDUCATION
MSc in Systems and Methods of Machine Learning 09/2025 – 12/2026
Igor Sikorsky Kyiv Polytechnic Institute (KPI)
Implemented Python-based solutions for MSE (Mean Squared Error) calculations
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and Back Propagation neural network testing.
Applied genetic algorithms to solve complex optimization problems.
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Utilized VS Code and Jupyter Notebooks for technical task execution.
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BSc in Mathematics 09/2021 – 05/2025
V. N. Karazin Kharkiv National University
•Strong foundation in Linear Algebra, Mathematical Statistics, Optimization
Methods, and Probability Theory.
WORK EXPERIENCE
IT Infrastructure Analyst 10/2024 – Present
Alesta UA Kyiv, Ukraine
Analyzed IT infrastructure requirements for B2B enterprise clients.
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Developed multi-level communication scripts for interacting with CTOs and
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procurement departments regarding Dell solutions.
Optimized communication workflows by structuring technical hardware
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advantages into logical blocks.
Prepared analytical reports and technical documentation in a strict, professional
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tone.
Decomposed project requirements and maintained analytical reporting workflows
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using spreadsheets.
Conducted system optimization and basic technical support for server hardware
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deployments.

CERTIFICATES
Natural Language Processing | Deep Learning | Machine Learning
DeepLearning.AI & Stanford DeepLearning.AI & Stanford Specialization |
University University DeepLearning.AI & Stanford
University