Introduction to Machine Learning for Biology (Genesis Protocol: Next Generation Technology for Biological and Life Sciences)

By Jamie Flux

Discover the power of machine learning in the field of biology with this comprehensive guide. With Python code provided for each chapter, "Introduction to Machine Learning for Biology" offers a hands-on approach to learning and implementing machine learning techniques in biological research and analysis. Key Features: - Covers a wide range of machine learning algorithms, from linear regression to quantum machine learning - Provides practical examples and Python code for each algorithm - Explores applications of machine learning in various areas of biology, including genetics, bioinformatics, and image analysis - Includes explanations of fundamental concepts and techniques in machine learning Machine learning has revolutionized the field of biology, offering powerful tools for analyzing complex biological datasets and making predictions. "Introduction to Machine Learning for Biology" takes you on a journey through the most important machine learning algorithms and their applications in biology. Starting with the basics of linear regression and logistic regression, you'll learn how to fit lines and classify data in biological contexts. Expanding on these foundations, the book then introduces support vector machines, decision trees, random forests, and k-nearest neighbors, with examples showcasing their usage in various biological problems. The book also delves into more advanced topics, such as neural networks, convolutional neural networks for analyzing biological images, and recurrent neural networks for processing sequential data like DNA sequences. You'll also explore dimensionality reduction techniques, including principal component analysis and t-distributed stochastic neighbor embedding, and discover the power of clustering algorithms such as k-means and hierarchical clustering. Throughout the book, practical examples and Python code are provided to help you implement and apply these machine learning techniques to real-world biological problems. Whether you're a biologist looking to expand your data analysis skills or a data scientist interested in applying machine learning to biology, this book is an essential resource. What You Will Learn: - Understand the fundamentals of machine learning and its applications in biology - Gain practical experience in implementing and applying machine learning algorithms using Python - Explore a wide range of machine learning algorithms, from linear regression to quantum machine learning - Discover how machine learning can be used to analyze biological data, including gene expressions and biological images - Learn about dimensionality reduction techniques and clustering algorithms for biological data analysis Who This Book Is For: This book is perfect for biologists, bioinformaticians, and data scientists who want to learn how to apply machine learning techniques to biological data. Basic knowledge of biology and programming (preferably Python) is recommended. The provided Python code makes it easy for readers to implement the algorithms and follow along with the examples.

• Format: Paperback
• Language: English
• ISBN: B0DF75YF37
• ISBN13: 9798336859874
• Release Date: August 2024
• Publisher: Independently published
• Length: 211 Pages
• Weight: 0.84 lbs.
• Dimensions: 9.0" x 0.5" x 6.0"