- Data Preprocessing
- Convolutional Neural Networks
- Machine Learning
- Artificial Neural Networks
- Health Policy
- Responsible AI
- Model Evaluation
- Artificial Intelligence and Machine Learning (AI/ML)
- Reinforcement Learning
- Healthcare Ethics
- Data Ethics
- Deep Learning
Fundamentals of Machine Learning for Healthcare
Completed by Panagiotis Baras
March 7, 2021
14 hours (approximately)
Panagiotis Baras'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
