Titlebook: Embedded Machine Learning with Microcontrollers; Applications on STM3 Cem Ünsalan,Berkan Höke,Eren Atmaca Textbook 2025 The Editor(s) (if a

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https://doi.org/10.1007/978-3-7091-2606-6hon running on PC. Python will allow the reader to implement and test the desired machine learning algorithm offline. Besides, it will help the reader to visualize data obtained from sensors. Hence, it can be analyzed easily. Our second perspective will be from the microcontroller side. The microcon

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Physical Nature of Thermography,udosensor data. This will help us while testing machine learning methods when there is no active sensor around. Then, we will consider feature extraction as a way of summarizing the acquired sensor data as the second concept. This will act as a bridge between Chap. 4 on data acquisition and the foll

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R. A. Olsson,R. D. Thompson,S. Kusachiusing the information on other variables. To fully explain the regression concept, we will start with its definition in this chapter. Then, we will introduce linear, polynomial, kNN, and decision tree regression. While handling each regression method, we will cover its theoretical background. Then,

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Validation and Generalization of CTM in classification and regression. In order to fully explain the clustering concept, we will start with its definition. Then, we will introduce the k-means and DBSCAN clustering algorithms. These algorithms have different working principles. Hence, they provide different clusters for the same data a