Independently Published
Machine Learning : Master Machine Learning For Aspiring Data Scientists
$16.46
Springer
Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data
Product Code:
9783319663074
ISBN13:
9783319663074
Condition:
New
$211.47
Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data
$211.47
This book explains the complete loop to effectively use self-tracking data for machine learning. While it focuses on self-tracking data, the techniques explained are also applicable to sensory data in general, making it useful for a wider audience. Discussing concepts drawn from from state-of-the-art scientific literature, it illustrates the approaches using a case study of a rich self-tracking data set. Self-tracking has become part of the modern lifestyle, and the amount of data generated by these devices is so overwhelming that it is difficult to obtain useful insights from it. Luckily, in the domain of artificial intelligence there are techniques that can help out: machine-learning approaches allow this type of data to be analyzed. While there are ample books that explain machine-learning techniques, self-tracking data comes with its own difficulties that require dedicated techniques such as learning over time and across users.
| Author: Mark Hoogendoorn |
| Publisher: Springer |
| Publication Date: Oct 05, 2017 |
| Number of Pages: 231 pages |
| Binding: Hardback or Cased Book |
| ISBN-10: 3319663070 |
| ISBN-13: 9783319663074 |
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