Machine Learning for Biomedical Applications: With Scikit-Learn and PyTorch

Machine Learning for Biomedical Applications: With Scikit-Learn and PyTorch provides a practical and interactive introduction to machine learning and data analysis skills for undergraduates and postgraduates in biomedical engineering, computer science, the biomedical sciences, and clinicians.

Format: Paperback / softback
Length: 304 pages
Publication date: 01 September 2023
Publisher: Elsevier Science Publishing Co Inc

Machine Learning for Biomedical Applications: With Scikit-Learn and PyTorch

Machine learning has revolutionized the field of biomedical applications, enabling researchers to analyze vast amounts of data and make accurate predictions. In this comprehensive guide, we will explore the most commonly used machine learning techniques in a biomedical setting, providing a practical and interactive way of learning.

We will begin by avoiding a theoretical perspective and focusing on practical applications. Concepts will be presented in short descriptions, followed by simple examples using biomedical data. Interactive Python notebooks will be provided with each chapter to complement the text and aid understanding.

Sections will cover a wide range of topics, including:

Uses in biomedical applications: Learn how machine learning is being used to diagnose diseases, develop new treatments, and improve patient outcomes.

Practical Python coding skills: Develop the skills needed to implement machine learning algorithms in Python, including data manipulation, model training, and evaluation.

Mathematical tools that underpin the field: Understand the mathematical principles that drive machine learning, such as probability, statistics, and linear algebra.

Core machine learning methods: Explore the most popular machine learning algorithms, including supervised learning, unsupervised learning, and reinforcement learning.

Deep learning concepts with examples in Keras: Learn how to implement deep learning models using the popular deep learning framework Keras, with examples in biomedical applications.

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. Whether you are a beginner or an experienced researcher, this guide will help you unlock the power of machine learning and apply it to real-world biomedical problems.

So, what are you waiting for? Start your journey into machine learning for biomedical applications today!

Weight: 1000g
Dimension: 235 x 191 (mm)
ISBN-13: 9780128229040