How to Build Machine Learning with Google Prediction API

While not widely understood, machine learning has been easily accessible since Google Prediction API was released in 2011. With many applications in a wide variety of fields, this tutorial by Alex Casalboni on the Cloud Academy blog is a useful place to start learning how to build a machine learning model using Google Prediction API. The API offers a RESTful interface as a means to train a machine learning model, and is considered a "black box" due to the restricted access users have to internal configuration. This leaves users with only the "classification" vs "regression" configuration, or the applying of a PMML (Predictive Model Markup Language) file with weighting parameters for categorical models. This tutorial begins with some brief definitions before beginning on how to upload your dataset to Google Cloud Storage, as required by Google Prediction API.