Applied Machine Learning for Health and Fitness: A Practical Guide to Machine Learning with Deep Vision, Sensors and IoT

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Applied Machine Learning for Health and Fitness: A Practical Guide to Machine Learning with Deep Vision, Sensors and IoT

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Applied Machine Learning for Health and Fitness is a comprehensive guide to using machine learning methods with deep computer vision, sensors and data in sports, health and fitness and other industries. Accompanied by practical step-by-step Python code samples and Jupyter notebooks, it acts as a reference for a data scientist, machine learning practitioner or anyone interested in AI applications. These ML models and methods can be used to create solutions for AI enhanced coaching, judging, athletic performance improvement, movement analysis, simulations, in motion capture, gaming, cinema production and more.

Packed with fun, practical applications for sports, machine learning models used in the book include supervised, unsupervised and cutting-edge reinforcement learning methods and models with popular tools like PyTorch, Tensorflow, Keras, OpenAI Gym and OpenCV. Author Kevin Ashley―who happens to be both a machine learning expert and a professional ski instructor―has written an insightful book that takes you on a journey of modern sport science and AI. 

Filled with thorough, engaging illustrations and dozens of real-life examples, Applied Machine Learning for Health and Fitness is your next step to understanding the implementation of AI within the sports world and beyond. Whether you are a data scientist, a coach, an athlete, or simply a personal fitness enthusiast excited about connecting your findings with AI methods, the author’s practical expertise in both tech and sports is an undeniable asset for your learning process. Today’s data scientists are the future of athletics, and Applied Machine Learning for Health and Fitness hands you the knowledge you need to stay relevant in this rapidly growing space.

What Readers Will Learn:

  • Apply machine learning and AI methods on sports data
  • Use multiple data science tools and frameworks: Python, Jupyter notebooks, OpenCV, PyTorch, Keras, Tensorflow, OpenAI Gym, physics libraries and cloud tools
  • Apply deep computer vision and other machine learning methods for classification, semantic segmentation, action recognition, detection, pose estimation, skill detection
  • Build and train neural networks, reinforcement learning models and more
  • Analyze multiple sporting activities with deep learning
  • Use datasets available today for model training
  • Use machine learning in the cloud to train and deploy models
  • Apply best practices in machine learning and data science

    Who This Book Is For:

    Primarily aimed at data scientists, coaches, sports enthusiasts and athletes interested in connecting sports with technology and AI methods. With this book machine learning becomes fun and engaging, when applied to real life scenarios of athlete performance. This book tells a unique story of combining advanced machine learning, AI methods with practical questions coaches and sports technologists ask every day.



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