Machine Learning for Healthcare

This course will explore the conceptual aspects of applying machine learning to problems in the healthcare industry, discuss case studies of machine learning used in healthcare, and explore practical implementations of techniques on real-world data from that industry.

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Course Insight

Suitable for beginner learners. This course serves as an entry point into Data Science, building foundational knowledge before moving on to advanced frameworks or specialized paths.

Beginner FriendlySelf-Paced LearningHands-On Learning

SKILLS TO
MASTER

Analytics
Exploratory Data Analysis
ModelingTrending
Predictive Machine Learning
SQL Querying
Relational Data Management
Pandas
Matplotlib
Statistics
Tableau
ETL
Careers:Relevant for professionals pursuing roles within Data Science.

Quick Facts

Below sections are verified from last major sync. For real-time updates and today's latest lectures, Check official page here.

What You’ll Learn

  • Course Overview : 1min.
  • Exploring Applications of Machine Learning in Healthcare : 36mins.
  • Case Study: Disease Detection Using Machine Learning : 20mins.
  • Case Study: Diagnosis Using Medical Imaging : 18mins.
  • Applying Machine Learning Techniques to Healthcare Data : 31mins.
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Description

The healthcare industry generates vast quantities of data, and so presents unique opportunities for applying machine learning. The use of machine learning in healthcare can prove transformative in the lives of people around the world.

In this course, Machine Learning for Healthcare, you'll explore machine learning techniques currently applied in the healthcare industry. First, you'll explore a few specific use cases such as the use of ML techniques for epidemic control, AI-assisted robotic surgery, patient diagnosis, and the automation of administrative tasks. You will also get an intuitive understanding of how convolutional neural networks work and how they are used in medical imaging.

Next, you will understand the steps involved in applying machine learning techniques to chronic disease prediction. You will study a case from a research paper that uses natural language processing and text extraction techniques on medical notes to diagnose chronic diseases for hospital patients. Another case study will discuss the use of medical imaging and image preprocessing techniques to detect leukemia from microscopic blood cell images.

Finally, you will get hands-on coding and see how you can use regression models to predict blood pressure and classification models to predict liver disease.

When you are finished with this course you will have the awareness of how machine learning can be applied in the healthcare industry and hands-on experience working with healthcare data.

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Machine Learning for Healthcare
4(13+ learners)