A video training course on AI in Sports with Python. Learn and apply Artificial Intelligence with fun examples.

A video training course on AI in Sports with Python. Learn and apply Artificial Intelligence with fun examples.

AI in Sports with Python is a fun new video course that complements and extends my book 'Applied Machine Learning for Health and Fitness' through videos and new examples. Learn AI with Python with fun applications in many sports: tennis, surfing, skiing, snowboarding, skateboarding, football, gymnastics, basketball, track and field and much more!

It is for anyone interested in applications of AI and data science for sports, health and fitness, and analysis of human motion.

-- Phil Cheetham, US Olympic Team scientist and twice Olympian


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AI in Sports with Python (Video Course)

AI in Sports with Python is a fun new video course that complements and extends my book 'Applied Machine Learning for Health and Fitness' through videos and new examples. Learn AI with Python with fun applications in many sports: tennis, surfing, skiing, snowboarding, skateboarding, football, gymnastics, basketball, track and field and much more!

Book and Table of Contents

I found it works best when you watch the video and use the book as a reference.

If you are an experienced data or sport scientist or a hobbyist, looking to understand AI better, this book should give you plenty of inspiration and practical examples.

-- Phil Cheetham, PhD, US Olympic team sport scientist and twice Olympian


Table of Contents

Machine Learning in Sports 101 (Supervised, unsupervised, reinforcement learning – Logic and machine learning – Tools – Neural networks – Deep vision – Classification – Detection – Semantic segmentation – Sensors – Reinforcement learning)

Physics of Sports (Mechanics – Kinetics – Laws of motion – Inertia – Kinematics – Projectile motion – Using neural networks to predict a projectile range – Angular motion – Conservation laws – Energy, work and power – Physics and deep learning)

Sports Scientist Toolbox (Data science tools – Python – Virtual environments – Packaging – Anaconda – Pip – Jupyter notebooks – Numpy – Pandas – Visualizations – Matplotlib – SciPy – Scikit-image – OpenCV – PyTorch – Keras – Tensorflow – OpenAI Gym – Pybullet)

Neural Networks (Neurons – Activation – Perceptron – Training a perceptron in Python – Multi-layer networks – Backpropagation)

Sensors (Deep Vision – Edge devices – Inertial movement sensors IMUs – Attitude and heading reference systems AHRS – Inertial and navigation systems GNSS – Range Imaging Sensors LIDAR – Pressure sensors – EMG sensors – Heart rate sensors )

Deep Computer Vision (Neuroscience and deep learning – Computer vision – Visual datasets – Model zoo – Applying models – Classification – Classifying sport activity type – Detection – Segmentation –Semantic segmentation – Human body keypoint detection)

2D Pose Estimation (Methods – Neural networks – Datasets – Tools – Body pose estimation – Detecting athlete stance – Activity recognition – Detecting skill level – Multi-person pose estimation – Dealing with loss and occlusion)
3D Pose Estimation (Cameras and 3D – Camera Matrix – 3D Reconstruction – Using a single camera – Multi-view depth reconstruction – 3D reconstruction with sensors – Motion capture – 3D Datasets – 3D Machine learning methods – Sparse and dense reconstruction)

Video Action Recognition (Video Data – Datasets – Models – Video classification – Action recognition – Loading videos for classifier training – Visualizing datasets – Video normalization – Training video recognition model)

Reinforcement Learning (Tools – Applying reinforcement learning in sports – Action and observation spaces – Visualizing sample motion – Model zoo – Models – Reinforcement learning in gymnastics – Pendulum model – Humanoid models – Joints and action spaces – Human motion capture – Mocap – Reinforcement learning in humanoids)

Machine Learning in the Cloud (Containers – Docker – Notebooks in the cloud – Data storage and datasets in the cloud – Loading and accessing datasets – Labeling data in the cloud – Training classification model – Preparing for training – Running experiments – Model management)

Automating and Consuming Machine Learning (CI/CD – MLOps – Managing models – Creating a scoring script – Defining an environment – Deploying models – Consuming models – APIs – Machine learning pipelines)

Episodes

This course is for anyone interested in AI, data science, gadgets and Python. Whether you are new to machine learning or an experienced data scientist, the course contains practical examples for all levels: beginners and advanced. The goal is to make AI easy to learn. My book has been reviewed by Olympic level coaches, sport scientists and machine learning experts. Since this is a video course, I want to take advantage of the video format and plan for some bonus stretch examples and exercises.

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