Machine Learning for Life Science

The proposed series explores fundamental concepts, establishing a solid foundation by elucidating core algorithms and methodologies in accessible language. They will feature extensive examples and case studies implemented in R, MATLAB, and Python, enabling readers to acquire hands-on expertise across diverse programming environments. This series aims to provide a holistic learning experience, fostering both conceptual comprehension and practical proficiency. It discusses machine learning for tasks such as image processing, analysis of forest fires and deforestation, and identification of hills and other landforms will contribute to the series’ value.