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Coursera

Computer Vision and Sequence Analysis in Machine Learning

via Coursera

Overview

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This course explores the foundational and applied aspects of machine learning techniques used to analyze image and time-series data, with a focus on healthcare applications. Learners will gain hands-on experience in designing models that detect brain tumors from MRI scans and predict clinical events such as sepsis onset using patient vital signs. You’ll also gain exclusive insights from a now-retired, globally recognized pioneer in medical technology—whose decades-long career shaped the field and who now shares hard-earned wisdom to inspire and guide the next generation of innovators. This course is ideal for: • Healthcare professionals (e.g., clinicians, nurses, administrators) looking to understand how AI and machine learning can enhance patient care and operational efficiency. • Data scientists and analysts working in or transitioning to the healthcare industry. • Students and researchers in fields like biomedical engineering, public health, or health informatics who want a practical introduction to ML in clinical contexts. • Healthcare innovators and tech entrepreneurs aiming to build or evaluate AI-driven healthcare solutions.

Syllabus

  • Computer Vision: Images and the Shape of Their Data
    • The first module explores images and the vital role their data structure plays in computer vision.
  • Building Blocks of Computer Vision
    • In the second module, we explore more building blocks of computer vision and begin working with real-life datasets.
  • Sequence Analysis Part 1: Time Series
    • This module introduces learners to time series analysis using real-world datasets focused on human activity.
  • Sequence Analysis Part 2: State Transitions
    • This module introduces advanced techniques for identifying state transitions in time series data.

Taught by

Ghaith Habboub, MD

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