Step Forward Feature Selection: A Practical Example in Python

[unable to retrieve full-text content]When it comes to disciplined approaches to feature selection, wrapper methods are those which marry the feature selection process to the type of model being built, evaluating feature subsets in order to detect the model performance between features, and subsequently select the best performing subset.
Original Post: Step Forward Feature Selection: A Practical Example in Python

IoT on AWS: Machine Learning Models and Dashboards from Sensor Data

[unable to retrieve full-text content]I developed my first IoT project using my notebook as an IoT device and AWS IoT as infrastructure, with this “simple” idea: collect CPU Temperature from my Notebook running on Ubuntu, send to Amazon AWS IoT, save data, make it available for Machine Learning models and dashboards.
Original Post: IoT on AWS: Machine Learning Models and Dashboards from Sensor Data