Machine Learning for Biomedical Applications

Machine Learning for Biomedical Applications 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 solving simple examples using biomedical data. Interactive Python notebooks are provided with each chapter to complement the text and aid understanding.

The book is divided into four Parts: A general background to machine learning techniques and their use in biomedical applications, practical Python coding skills, and mathematical tool that underpin the field; core machine learning methods; Deep learning concepts with examples in Keras. ; tricks of the trade where guidance is given on best practice for data preparation and experimental design to aid the successful application of machine learning methods to real world biomedical data.

This accessible and interactive introduction to machine learning and data analysis skills is suitable for undergraduates and postgraduates in biomedical engineering, computer science, biomedical science, and clinicians.

• Gives a basic understanding of the most fundamental concepts within machine learning and their role in biomedical data analysis
• Shows to apply a range of commonly used machine learning and deep learning techniques to biomedical problems
• Develops practical computational skills that are needed to manipulate complex biomedical data sets
• Shows how to design machine learning experiments that address specific problems related to biomedical data