This article provides a hands-on guide to classifying human activity using sensor data and machine learning. It covers preparing data, creating a feature extraction pipeline using TSFresh, training a machine learning classifier with scikit-learn, and validating the model using the Data Studio.
A detailed overview of the architecture, Python implementation, and future of autoencoders, focusing on their use in feature extraction and dimension reduction in unsupervised learning.
This article provides a step-by-step guide on how to extract meaningful features from graphs using NetworkX for machine learning applications. It uses Zachary's Karate Club Network as an example and covers feature extraction at node, edge, and graph levels.
emlearn is an open-source machine learning inference engine designed for microcontrollers and embedded devices. It supports various machine learning models for classification, regression, unsupervised learning, and feature extraction. The engine is portable, with a single header file include, and uses C99 code and static memory allocation. Users can train models in Python and convert them to C code for inference.
