Machine Learning for Biomedical Applications: With Scikit-Learn and PyTorch presents machine learning techniques most commonly used in a biomedical setting. Avoiding a theoretical perspective, it provides a practical and interactive way of learning where concepts are presented in short descriptions followed by simple examples using biomedical data. Interactive Python notebooks are provided with each chapter to complement the text and aid understanding. Sections cover uses in biomedical applications, practical Python coding skills, mathematical tools that underpin the field, core machine learning methods, deep learning concepts with examples in Keras, and much more.
This accessible and interactive introduction to machine learning and data analysis skills is suitable for undergraduates and postgraduates in biomedical engineering, computer science, the biomedical sciences and clinicians.
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Table of Contents
1. Programming in Python2. Machine Learning Basics
3. Regression
4. Classification
5. Dimensionality reduction
6. Clustering
7. Ensemble methods
8. Feature extraction and selection
9. Introduction to Deep Learning
10. Neural Networks
11. Convolutional Neural Networks