Machine Learning for Smart Health Systems

Machine Learning for Smart Health Systems

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Start Date: November 1, 2021

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Course duration: 30 hours

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Cost: donation

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Skill level: beginner

Course Description

For whom is this course

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This course is for anyone interested in applying machine learning for disease detection, monitoring, and other biomedical applications.

Objective

  • Learn how to process physiological signals e.g. ECG, EEG, PPG, respiration captured from the human body
  • Feature extraction from physiological signals
  • Feature selection for the detection of specific diseases of interest
  • Importance of confounding factor analysis in biomedical applications
  • Disease classification and severity estimation using machine learning and deep learning models

What you will learn

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  • Technical skills are essential, but not enough, non-technical and domain fields of studies are still essential if you want to understand data science vs its application.
  • Current and future global challenges in the sector
  • How data science or artificial intelligence would be applied.
  • Data science and the necessities to keep learning for life.
  • Instructor-led online course with guided labs
  • Real-world, practical assignment(s) leading to project
  • Application in biomedical and health systems

Prerequisites

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  •  Basic Python

Syllabus

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WeekInstruction
(1 hr)
Lab (guided + unguided)
1+ 3 hrs
Week 1
(5 hrs)
Intro to smart health, and physiological signals.
ETL and visualization in smart health
Basic signal processing-loading, denoising ECG, acceleration, PPG signal and detecting biomarkers
Week 2
(5 hrs)
Feature extraction, Feature selection in machine learning, and co-variate analysisExtract features from ECG and PPG, acceleration signals, Feature selection for sleep apnea & heart disease classification, and gait analysis
Week 3
(5 hrs)
Developing machine learning & deep learning models for disease classificationSleep apnea and heart disease classification using ensemble and deep neural networks
Week 4
(5 hrs)
Deploying machine learning & deep learning models for disease classificationExport portable and deployable model using WEKA & TF-Lite, cloud computing
Week 5
(10 hrs)
Case study guidance & evaluationReal-world case study (10 hrs)

Course Features

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Lectures: Updating

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Duration: 30 hours

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Students: 35-40

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Certificate: yes

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Cost: donation

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Skill level: beginner

Video

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Instructor

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Juber Rahman, Ph.D.

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