Comprehensive coverage of animal behavior and ethology concepts for a solid foundation
Step-by-step practical guide to data collection, preprocessing, and model training for hands-on learning
Detailed exploration of supervised, unsupervised, semi-supervised, and reinforcement learning techniques
Python implementations and code examples for immediate application and experimentation
Advanced topics such as feature selection, model selection, hyperparameter tuning, and deep learning covered
Summarized by Shop
This book is a comprehensive guide to applying machine learning to animal behavior analysis, focusing on activity recognition in farm animals. It begins by introducing key concepts of animal behavior and ethology, followed by an exploration of machine learning techniques, including supervised, unsupervised, semi-supervised, and reinforcem
Format
E-Book
Language
English
Primary Programming Language
Python
Target Audience
Researchers, students, professionals in animal science and machine learning
Key Topics
Animal behavior analysis, activity recognition, machine learning techniques, data collection, feature extraction, model evaluation