This include collecting the samples by scraping a website and extracting data, from an RSS feed or an API. Use necessary sensors or make use of publicly available data.
Prepare Input Data
Once data is collected make sure its in a useable format. The benefit of having this standard format is that you can mix and match algorithms and data sources.
Analyse the input data
Is looking at the data from previous task. It involves recognizing patterns, identifying outliers and detection of novelty.
Train The Algorithm
The actual machine learning tasks place here. This step and the next step are where the “core” algorithms lie, depending on the algorithm. Feed the algorithm good clean data from the first who steps and extract knowledge or information. The knowledge extracted is stored in a format that’s readily usable by a machine for the next two steps.
Test The Algorithm
The information learned in the previous steps is put to use during testing.
Make use of real program to do some task, and once again you see if all the previous steps worked as you expected.