Machine Learning for Smart Health Systems

Machine Learning for Smart Health Systems
Start Date: November 1, 2021
Last date to register:
Course duration: 30 hours
Cost: donation
Skill level: beginner
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Course Description

For whom is this course

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

  • 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

  •  Basic Python

Syllabus

Week Instruction
(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 analysis Extract 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 classification Sleep apnea and heart disease classification using ensemble and deep neural networks
Week 4
(5 hrs)
Deploying machine learning & deep learning models for disease classification Export portable and deployable model using WEKA & TF-Lite, cloud computing
Week 5
(10 hrs)
Case study guidance & evaluation Real-world case study (10 hrs)

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Course Features

Lectures: Updating
Duration: 30 hours
Students: 35-40
Certificate: yes
Cost: donation
Skill: beginner
Register Closed

Video

Instructor

Juber Rahman, Ph.D.
Updating...

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