Machine Learning (ML) iteration is not the simplest tasks due to the fact that the requirements constantly move goalposts. Plus, due to a lack of standardization, there are no adopted benchmarks against which to deploy one’s latest development. Due to this reason, developers must make ample use of the existing tools for its development. Some such common tools include Scikit-Learn, Pandas and Feature-tools. There are three steps that need to be followed in the entire development cycle. To start with is Prediction Engineering. This is about setting up the ML problem. Next up is Feature Engineering which iterates how ML gets powered. And finally, there is the Modeling phase. This is the business end of things where one works with algorithms. This algorithm in turn uses the business analytics model to make predictions from the existing data of possibilities.

Source:https://towardsdatascience.com/how-to-create-value-with-machine-learning-eb09585b332e

Uploaded Date:10 November 2018