- Convolutional Neural Networks
- Artificial Neural Networks
- Responsible AI
- Reinforcement Learning
- Model Evaluation
- Artificial Intelligence and Machine Learning (AI/ML)
- Applied Machine Learning
- Deep Learning
- Data Ethics
- Machine Learning
- Machine Learning Algorithms
- Data Preprocessing
Fundamentals of Machine Learning for Healthcare
Completed by Lina Henglein
May 14, 2024
14 hours (approximately)
Lina Henglein'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
