Description
Machine Learning for Biomedical Applications provides a practical introduction to applying machine learning algorithms in healthcare and biomedical research. Written by Maria Deprez and Emma C. Robinson, this comprehensive text covers essential techniques using two of the most popular Python libraries: Scikit-learn for traditional machine learning and PyTorch for deep learning.
The book progresses from fundamental concepts to advanced applications, demonstrating how to preprocess biomedical data, build predictive models, and evaluate performance metrics specific to healthcare contexts. Readers will learn to implement classification and regression models, work with medical imaging data, and develop neural networks tailored for clinical applications.
Ideal for data scientists, biomedical engineers, and healthcare researchers, this resource combines theoretical foundations with hands-on code examples. Each chapter includes practical exercises using real-world datasets, ensuring readers can immediately apply their knowledge to pressing biomedical challenges and contribute to advancing healthcare through machine learning innovation.
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