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.
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 LearningPythonArtificial Intelligence (AI)Cert PrepOpen Source
Concepts
Introduction
- Understanding machine learning in healthcare
- What you should know
Fundamentals of Machine Learning for Healthcare
- Machine learning, artificial intelligence, and data science
- Applications of machine learning in healthcare
- How to think about machine learning in healthcare
- Machine learning vs. rule-based programming in healthcare
- Types of machine learning in healthcare
- Healthcare data types for machine learning
- Features and labels in machine learning for healthcare
- Machine learning models and algorithms in healthcare
- Deep learning models and architecture in healthcare
- Transfer learning and pretrained models in healthcare
- Assessment metrics for machine learning models
- Tools and libraries for machine learning
- Data privacy and ethics in healthcare machine learning
- Career opportunities in machine learning for healthcare
Machine Learning for Tabular Healthcare Data
- How to use a Google Colab Notebook
- Explore the heart failure clinical record dataset
- Classification task - Heart failure outcomes prediction with no feature scaling
- Classification task - Heart failure outcomes prediction with feature scaling
- Regression task - Predict the heart rejection fraction
- Feature importance in regression tasks
Machine Learning for Tabular Healthcare Data - Unsupervised
- Clustering task - Localization data for person activity
- Dimensionality reduction - Localization data for person activity
Conclusion
- Next steps