• 🎓 I’m an AI student at Poznan University of Technology
• 🌱 I’m currently learning Machine Learning, Deep Learning, and Data Analysis
• 👀 I’m interested in all things Artificial Intelligence and Data Science
• 💼 I’m looking to collaborate on Open Source projects and innovative AI solutions

• Languages: Python, SQL
• Libraries & Frameworks: TensorFlow, PyTorch, Scikit-Learn, Pandas, NumPy, Matplotlib
• Tools: Jupyter Notebook, GitHub, Docker

During my studies, I've tackled a variety of projects that have allowed me to apply classroom theories in real-world scenarios. Below are some key projects that showcase my skills and learning:

• Project Title: Nvidia Jetbot

  • Description: Developed an autonomous driving system for the NVIDIA Jetbot. The system enables the Jetbot to navigate roads and obstacles using Convolutional Neural Networks (CNNs) implementing an end-to-end driving algorithm. This project covers data collection, model training, and deployment on the Jetbot to achieve autonomous navigation.
  • Explore the colab for the model part of the project

• Project Title: Toy Robot Controller Using iPhone Camera

  • Description: Developed a system to control a toy robot via Bluetooth LE, utilizing image processing techniques from the iPhone camera. The system enables the robot to move in response to visual inputs, such as detecting and following a red object. This project covers setting up the camera feed, processing the images to determine movement directions, and communicating with the robot via Bluetooth to execute these movements.
  • Tools: Python, opencv, numpy, asyncio
  • Explore the project

• Project Title: Association Rules-based Movie Recommender
  • Description: Developed a recommendation system that uses association rules to suggest movies based on user preferences and avoid potential dislikes, enhancing viewer satisfaction.
  • Tools: Python, Pandas, mlxtend
  • Explore the project

• Project Title: Dynamic Article Recommendation Engine
  • Description: Engineered a recommendation system using machine learning to suggest articles to users of the "Rick and Morty" fandom wiki. Implemented features include text similarity-based dynamic suggestions, efficient data processing, and user-engaging visualizations.
  • Tools: Python, BeautifulSoup, spaCy, Scikit-learn, Matplotlib
  • Explore the project

• Project Title: Economic Event Analysis

  • Description: Encompassed advanced natural language processing techniques to analyze economic events through fine-tuned language models and classify Federal Reserve speeches. The analysis utilizes state-of-the-art machine learning models tailored for financial contexts.
  • Tools: pytorch, huggingface transformers, nltk
  • Explore the project

• Project Titile: Sequence Defect Prediction and Localization

  • Description: Utilized LSTM networks to predict and localize defects in sequence data, demonstrating handling of variable sequence lengths and batch processing.
  • Tools: TensorFlow, Keras, Scikit-Learn, Pandas, NumPy
  • Explore the project

• Project Title: Enhancing CNNs for Robust Image Classification

  • Description: Developed multiple convolutional neural network (CNN) architectures to classify images from the '101 Object Categories' dataset. Implemented techniques like dropout, regularization, and data augmentation to enhance model accuracy and robustness against overfitting.
  • Tools: TensorFlow, Keras, OpenCV, Matplotlib, NumPy
  • Explore the project

During the Decision Analysis course, I learned to construct and apply various Multi-Criteria Decision Analysis (MCDA) tools for decision aid. We have presented some of these tools in the form of Google Colab notebooks avalilable here.

What I learned:

• data collection
• custom plotting with matplotlib
• efficient calculations with numpy
• MCDA tools for decision aid

During Software Engineering course, I gained knowledge and experience concerning various concepts retalted to the desing and maintainance of software.

• agile development: I was a scrum master for our course project (Link to our course project)
• git
• functional/non-functional requirements
• CI/CD
• UML notation
• Design Patterns, Architectural Patterns
• Testing: unit testing, automated testing using Selenium, performance testing
• REST API

During this course, I gained the knowledge and experience with:

• programming in Java
• writing clean, object oriented code
• creating GUI using JavaFX

Link to the course project