Portfolio

Here you can familiarize yourself with my theoretical knowledge and practical skills. All materials in subsections are arranged in a hierarchical structure.

Books:

• How Linux works
• Python for Data Analysis
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
• Head First Design Patterns: A Brain-friendly Guide

Courses:

• Machine Learning Specialization:
  • Supervised Machine Learning: Regression and Classification
  • Advanced Learning Algorithms
  • Unsupervised Learning, Recommenders, Reinforcement Learning
• Deep Learning Specialization:
  • Neural Networks and Deep Learning
  • Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
  • Structuring Machine Learning Projects
  • Convolutional Neural Networks
  • Sequence Models
• Intro to Deep Learning with PyTorch
• DataRoot University:
  • Data Science Fundamentals
  • Practical Deep Learning with PyTorch
• LP NLP 2023: Syllabus
• Generative Adversarial Networks (GANs) Specialization:
  • Build Basic Generative Adversarial Networks (GANs)
  • Build Better Generative Adversarial Networks (GANs)
  • Apply Generative Adversarial Networks (GANs)
• Machine Learning Engineering for Production (MLOps) Specialization:
  • Introduction to Machine Learning in Production
  • Machine Learning Data Lifecycle in Production
  • Machine Learning Modeling Pipelines in Production
  • Deploying Machine Learning Models in Production
• Computer Vision School: Advanced
• Вступ до MLOps/LLMOps

Projects:

Computer Vision:

• Cifar10
• RSNA
• Soil erosion detection
• VIN characters classification
• Airbus
• face_regocnition
• panoptic-segmentator

NLP:

• QuoraInsincereQuestionsClassification

Tabular:

• Marketplace Service Department Analysis
• Regression problem on tabular data
• 30 day LifeTime prediction
• Credit-Card-Churn

Sources:

• Data Science interview questions and answers
• The Unreasonable Effectiveness of Recurrent Neural Networks
• Design Patterns
• Refactoring