Machine Learning in Healthcare
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About This Course
Two communities, machine learning and healthcare, came together for a mission. To revolutionize our quality of life. Artificial Intelligence (AI), machine learning, and deep learning are taking the healthcare industry by storm. Machine learning lends itself to many processes better than others. Nearly all major companies in the healthcare space have already begun to use the technology in practice.
Why you should take this couse?
Rapidly growing industry
40% compound annual growth of AI in the healthcare industry
Most In-demand Job Skills
Glassdoor shows that data scientists have the No. 1 job in most countries
Rewarding Career Path
Rewarding career that is impactful on human being’s quality of life
Who this course is for
Learning Outcome
Upon completing the course, students will be able to:
- 01. Understand ML Applications in Healthcare
- 02. Machine Learning Techniques for Healthcare
- 03. Unsupervised Learning
- 04. Supervised Learning
- 05. Neural Networks
Introduction to ML in Healthcare
In module one, you will learn some important concepts in healthcare, Python programming and software development. In the next modules, we will work with different machine learning techniques and all the examples and data we will use will be related to healthcare.
- Introduction to genetics/genomics
- Introduction to imaging technologies and radiomics
- Introduction to programming in Python
- Introduction to version control
- Workflow management
Feature Preparation for Machine Learning
In module two, we work on feature selection and extraction techniques and start working with unsupervised machine learning models.
- Data normalization and outlier detection
- Feature selection and extraction in genomics and radiomics
- Introduction to Supervised Learning
Dimension Reduction and Clustering
In module three, we go into details of unsupervised machine learning modeling to cover different dimensionality reduction and clustering algorithms.
- Dimensionality reduction in genomics and radiomics
- Classical clustering algorithms with application in health
Learn Classification Models for Healthcare
In module four, we learn about supervised modeling in python covering both regression and classification methods. We also cover important aspects of modeling survival data.
- Regression versus classification
- Cross-validation; bias and overfitting
- Model performance measures
- Survival analysis in genomic and radiomics
Deep Learning for Healthcare
In module five, we will focus on neural network modeling to learn about the application of fully connected, convolutional and recurrent neural networks in healthcare.
- Introduction to neural network for supervised learning
- Deep learning for clinical image classification
- Image segmentation
Course Resources
Check out the videos and links below for more details about how we teach. Make sure you fill out the information to receive the course package that has all the details you need.
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What students are saying
Finally a well-designed Machine Learning course for Healthcare professionals. I like the breadth of this course. The instructors are absolutely amazing.
Jack
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