Unlock the power of your self-tracking data with "Machine Learning for the Quantified Self." This comprehensive guide teaches you the complete loop to effectively use sensory data for machine learning, providing valuable insights from your personal data. While focused on self-tracking, the techniques are applicable to all sensory data. Explore concepts from cutting-edge scientific literature, illustrated with a rich self-tracking data set case study. Self-tracking devices generate vast amounts of data, making it challenging to extract meaningful information. This book introduces machine-learning approaches specifically designed to analyze this complex data, addressing unique challenges like learning over time and across users. A must-read for anyone looking to leverage their data for advanced insights. Summary for keyword optimisation: Machine Learning for the Quantified Self, Self-Tracking Data, Sensory Data Analysis, Machine Learning Techniques, Artificial Intelligence, Hardcover Book, Computing & Internet, English Edition, Burkhardt Funk, Springer International Publishing AG, ISBN 9783319663074, 248 pages.