To achieve full understanding of the use and application of ML algorithms, our participants will work on a real-life industry project, translating theoretical knowledge to practical process and overcoming realistic challenges.
Scope:~400 work hours total
Data:Real data provided by company
Guidance:Experienced mentors provided by Y-DATA
Support:Weekly meetings with company data-owner
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Chronic wounds are a critical issue believed to affect as much as two per cent of the population. Accurate wound measurement and monitoring are crucial for effective wound care, but traditional measuring techniques are imprecise and can cause discomfort. Healthy’s solution leverages a smartphone and machine vision algorithms to accurately measure wounds and track their progress, in order to improve the way wounds are treated and managed. In order to achieve this, a key task is to segment the wound from an image, and in order to achieve effective segmentation, it is necessary to correctly detect and remove environmental background. In this project, the goal is to develop a deep learning model for skin segmentation, capable of separating foreground (skin and wound) and environmental background in 2D images.
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The process of working on the project follows popular industry standards and methodologies and incorporates a growing set of tools the students possess to methodically understand and solve a real-world problem. Our students have a full-cycle data science project in their portfolio upon graduation, covering all industry-standard stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation.
Example ProjectAutomatic detection of low-value queries in technical Q&A forum
A customer operates a forum where programmers ask each other questions, provide answers and rate questions giving them \"ups\" and \"downs\". The forum has a core expert community that provides good answers and valuable insights. However, they often waste their time handling questions of little to no value: marking questions as duplicates and redirecting them, closing topics with incoherent or irrelevant questions etc. Because of this, the overall efficiency of the system suffers.