Machine Learning Fundamentals for Healthcare

1h 36mBeginner2024-07-11

Authors

Wuraola Oyewusi

Wuraola Oyewusi is an experienced data scientist, machine learning, and artificial intelligence professional.

Course details

There’s an increased demand to integrate AI and machine learning workflows into many different business sectors. This is especially true in today’s unique and constantly evolving global healthcare landscape.

In this course, instructor Wuraola Oyewusi provides an overview of how AI and machine learning can optimize healthcare processes, data analysis, health outcomes, and more. Along the way, gather insights drawn from real-world examples to address complex privacy and ethical considerations in the industry. Wuraola also shows you how to utilize machine learning for tabular healthcare datasets using a Google Colab Notebook, including clinical records, classification, predictions, regression, clustering, and localization.

Skills covered

Machine LearningPythonCert PrepArtificial Intelligence (AI)Open Source

Concepts

0. Introduction

01 - Understanding machine learning in healthcare
02 - What you should know

1. Fundamentals of Machine Learning for Healthcare

03 - Machine learning, artificial intelligence, and data science
04 - Applications of machine learning in healthcare
05 - How to think about machine learning in healthcare
06 - Machine learning vs. rule-based programming in healthcare
07 - Types of machine learning in healthcare
08 - Healthcare data types for machine learning
09 - Features and labels in machine learning for healthcare
10 - Machine learning models and algorithms in healthcare
11 - Deep learning models and architecture in healthcare
12 - Transfer learning and pretrained models in healthcare
13 - Assessment metrics for machine learning models
14 - Tools and libraries for machine learning
15 - Data privacy and ethics in healthcare machine learning
16 - Career opportunities in machine learning for healthcare

2. Machine Learning for Tabular Healthcare Data

17 - How to use a Google Colab Notebook
18 - Explore the heart failure clinical record dataset
19 - Classification task - Heart failure outcomes prediction with no feature scaling
20 - Classification task - Heart failure outcomes prediction with feature scaling
21 - Regression task - Predict the heart rejection fraction
22 - Feature importance in regression tasks

3. Machine Learning for Tabular Healthcare Data - Unsupervised

23 - Clustering task - Localization data for person activity
24 - Dimensionality reduction - Localization data for person activity

Conclusion

25 - Next steps