Welcome to the crossroads of biomedical science and data!

Here you can follow all my practical and personal projects as I transition into the world of data science and AI.

I'm currently enhancing my Machine Learning and Computer Vision skills, with a keen interest in industrial applications.

• Currently a Junior Data Scientist @BeCode.org
• Academic background in Biomedical Sciences
• Extensive experience in data management and clinical trials
• Always eager to learn and grow

• Description: CellScope is an advanced project that demonstrates my expertise in computer vision, machine learning, and healthcare applications. It's designed to assist in the identification and analysis of white blood cells in peripheral blood smear images. Key features include:

  • White Blood Cell Detection using object detection models (YOLO)
  • White Blood Cell Classification into different types (basophils, eosinophils, lymphocytes, monocytes, and neutrophils) using fastai
  • Comprehensive model evaluation metrics including confusion matrices, precision-recall curves, and F1 curves
  • User-friendly interface for image upload and analysis

• Technologies Used: YOLO, fastai, Streamlit, Python, Hugging Face Model Hub

• Live Demo: CellScope App

• GitHub Repository: CellScope

• Model Repositories:

  • Detection Model on Hugging Face
  • Identification Model on Hugging Face

This project showcases my ability to develop end-to-end machine learning solutions for real-world problems, particularly in the medical field. It demonstrates my skills in implementing complex computer vision models, creating intuitive user interfaces, and deploying machine learning applications.

• Description: This project showcases my skills in natural language processing, vector databases, and application deployment. I developed an interactive chatbot using OpenAI's GPT-3.5-turbo model to answer chess-related questions. The chatbot utilizes a FAISS vector store for efficient document retrieval, combining retrieved information with the language model to generate accurate responses. Key features include:

  • Interactive chat interface for chess-related queries
  • Efficient document retrieval using FAISS vector store
  • Integration of retrieved documents with GPT-3.5-turbo for contextual responses
  • Custom UI with tailored images and backgrounds
  • Deployment on Streamlit for easy access and interaction

• Technologies Used: OpenAI API, FAISS, Streamlit, Python

• Live Demo: Chess Tutor Chatbot

• Additional Info: About Page

• GitHub Repository: Chess Tutor Chatbot

This project demonstrates my ability to integrate advanced AI models with efficient data retrieval systems, create user-friendly interfaces, and deploy functional AI applications.

• Description: This project demonstrates my proficiency in deep learning and data scraping. It involves training a deep learning model to classify metal music samples into different subgenres using mel spectrograms. The project also includes exploratory data analysis and validation methods.
• Link: Metal Classifier Project

• Description: Solemates is a project where I tackled the challenge of imbalanced data by training an XGBoost model. I conducted extensive exploratory data analysis (EDA) and deployed the model on Streamlit for interactive visualization.
• Link: Solemates Project

• Description: Project Wiwino involved exploratory data analysis (EDA) on a Vivino dataset along with a presentation of the results. This project showcases my ability to derive insights from data and effectively communicate findings.
• Link: Project Wiwino

• Description: Immo-Eliza is a series of repositories related to a project involving scraping data from Immoweb, analyzing it, generating models, and eventually deploying them. These repositories showcase my end-to-end project development skills.
• Links: Immo-Eliza Deployment and Immo-Eliza Scraping

• Description: Wikipedia Scraper was my first scraping project, while Openspace Organizer was the first code I ever wrote. They represent my journey into coding and learning new technologies.
• Links: Wikipedia Scraper and Openspace Organizer

Upon completing the AI and Data Science course at BeCode, I aspire to pursue roles that align with my passion and expertise. The job offer detailed in this document perfectly encapsulates the type of work I envision myself doing post-graduation. With the technical skills acquired during the training, I am confident in my ability to excel in this role and contribute meaningfully to the organization's objectives.

Thank you for taking the time to explore my GitHub profile and delve into my motivations and aspirations. I am excited about the possibilities that lie ahead in my journey as a data scientist and look forward to collaborating on impactful projects in the future.

Feel free to connect with me here on GitHub, on LinkedIn or reach out via email at maarten.kn@gmail.com.

Best regards, Maarten