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Machine Learning in Healthcare (no coding required!)

 
Machine Learning in Healthcare (no coding required!) | Udemy NED πŸ‘πŸ‘πŸ‘

With machine learning, machines have no explicit instructions but extrapolate and determine patterns in large chunks of data. “Reasoning” is stored information combined with rules and is ...

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 by Eddie Jay

This course will teach you how to work with health data, using machine learning models to find actionable insights.

Through a step-by-step guided case study, you will learn practical skills that you can apply immediately!

We will use a case study: Opioid Abuse Prediction for a clinic

Topics we will cover:

  1. Health Data (sources, types, features, error handling)

  2. Logistics of machine learning

  3. What predictive model features are, and how to create them

  4. A statistical primer, highlighting key machine learning models and concepts

  5. Build a decision tree, logistic regression and random forest through

    1. Opioid abuse prediction case study

    2. KNIME (a free machine learning software, no coding required!)

  6. Assess model performance

  7. Output presentation and implementation

Who this course is for:
  • Beginner to Intermediate analysts of health data
  • Public health/Epidemiology/Bioinformatics analysts
  • Actuarial/Statistical analysts
Health Analytics Guru / Actuary

I have worked in the data rich healthcare space for over 17 years.

I'm passionate about pragmatically utilizing health data to find actionable insights, thereby improving patient care.

I've worked for a variety of health care organizations including insurers, hospitals, pharmaceutical companies and software startups.

I know how to get value out of healthcare data.



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