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.
1. Machine Learning for Biomedical Applications - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright - p. iv 5. Table of Contents - p. iv 6. Preface - p. xiii 7. Chapter 1 Programming in Python - p. 1 8. Chapter 2 Machine learning basics - p. 41 9. Chapter 3 Regression - p. 67 10. Chapter 4 Classification - p. 87 11. Chapter 5 Dimensionality reduction - p. 105 12. Chapter 6 Clustering - p. 139 13. Chapter 7 Decision trees and ensemble learning - p. 153 14. Chapter 8 Feature extraction and selection - p. 175 15. Chapter 9 Deep learning basics - p. 193 16. Chapter 10 Fully connected neural networks - p. 213 17. Chapter 11 Convolutional neural networks - p. 233 18. References - p. 271 19. Index - p. 275
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.
1. Machine Learning for Biomedical Applications - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright - p. iv 5. Table of Contents - p. iv 6. Preface - p. xiii 7. Chapter 1 Programming in Python - p. 1 8. Chapter 2 Machine learning basics - p. 41 9. Chapter 3 Regression - p. 67 10. Chapter 4 Classification - p. 87 11. Chapter 5 Dimensionality reduction - p. 105 12. Chapter 6 Clustering - p. 139 13. Chapter 7 Decision trees and ensemble learning - p. 153 14. Chapter 8 Feature extraction and selection - p. 175 15. Chapter 9 Deep learning basics - p. 193 16. Chapter 10 Fully connected neural networks - p. 213 17. Chapter 11 Convolutional neural networks - p. 233 18. References - p. 271 19. Index - p. 275
