Machine learning (ML) has gone mainstream. On the same operational spectrum but distinctly different from artificial intelligence (AI), ML algorithms act as the software used by AI solutions to help understand the world around them and create data-driven rulesets that result in reliable outcomes. As the terms machine learning and artificial intelligence make their way into public discourse, people are naturally curious about how these technologies impact their lives. Three applications of ML that you may encounter in everyday life are: route planning, email spam filtering and social media tagging. In route planning, python libraries help underpin Google Maps Web services with support for everything from the Directions API to the Distance Matrix, Elevation, and Geocoding APIs. In terms of e-mail filtering, detecting and deleting spam emails while leaving legitimate messages alone requires machine learning. Python can help extend the functionality of these filters with tools that let them write and implement functions for improved detection. In the case of social media tagging, machine learning with python also makes it possible for social media platforms to more accurately tag people and objects in social posts and can be used to suggest relevant captions or hashtags for media. While this work is impressive, there’s more that’s being done with ML and Python than covered here. To learn more, check out the resource accompanying this post.
Python Programming & Its Importance In Machine Learning provided by Accelebrate, a company offering courses in Python Data Science training