Machine Learning in Farm Animal Behavior using Python

This book is a comprehensive guide in the fields of machine learning and animal behavior. It explores the use of machine learning algorithms to understand the behavior of farm animals based on activity recognition. It begins with an introduction to fundamental concepts of animal behavior and ethology, establishing a foundation before exploring the importance and types of machine learning, including supervised, unsupervised, semi-supervised, and reinforcement learning. Readers will gain practical experience through hands-on guidance that covers data collection, preprocessing, exploratory data analysis, feature extraction, model training, and evaluation using Python. The book also looks into the use of various sensors and methods for data collection and annotation, emphasizing the importance of high-quality data. Key machine learning concepts and challenges are addressed, focusing on generalization and data-related issues. Advanced topics include feature selection techniques, model selection, hyperparameter tuning, and deep learning algorithms. Practical examples and Python implementations are provided throughout to aid in the understanding and application of these techniques. The book is a valuable resource for researchers, students, and professionals interested in applying machine learning to animal behavior analysis, offering insights into both theoretical and practical aspects of the field.