- Convolutional Neural Networks
- Artificial Intelligence and Machine Learning (AI/ML)
- Data Ethics
- Health Policy
- Model Evaluation
- Data Preprocessing
- Healthcare Ethics
- Artificial Neural Networks
- Machine Learning Algorithms
- Responsible AI
- Reinforcement Learning
- Machine Learning
Fundamentals of Machine Learning for Healthcare
Completed by John Paul O Ahme
January 7, 2024
14 hours (approximately)
John Paul O Ahme's account is verified. Coursera certifies their successful completion of Fundamentals of Machine Learning for Healthcare
What you will learn
Define important relationships between the fields of machine learning, biostatistics, and traditional computer programming.
Learn about advanced neural network architectures for tasks ranging from text classification to object detection and segmentation.
Learn important approaches for leveraging data to train, validate, and test machine learning models.
Understand how dynamic medical practice and discontinuous timelines impact clinical machine learning application development and deployment.
Skills you will gain
