|MACHINE LEARNING – WHY & HOW IT WORKS?|
MACHINE LEARNING – WHY & HOW IT WORKS?
Machine Learning (ML) is now becoming more pertinent to our daily lives because of increased amounts of data, cheaper storage and greater processing power.
So how do we define it?
Machine Learning refers to the techniques involved in dealing with vast data in the most intelligent fashion (by developing algorithms) to derive actionable insights. It is a method of data analysis that automates analytical model building.
Here are a few widely publicized examples of machine learning applications you may be familiar with:
Let’s have a look on what are the popular Machine Learning methods:
What is the Difference between AI and ML?
ML is a subset of AI where the machine is trained to learn from its past experience. The past experience is developed through the data collected. Then it combines with algorithms such as Naïve Bayes, Support Vector Machine (SVM) to deliver the final results.
Where can we use ML?
Looking for a more realistic Example? Here it is...
Face book’s News Feed uses machine learning to personalize each member's feed. If a member frequently stops scrolling to read or "like" a particular friend's posts, the News Feed will start to show more of that friend's activity earlier in the feed. Behind the scenes, the software is simply using statistical analysis and predictive analytics to identify patterns in the user's data and use those patterns to populate the News Feed. Should the member no longer stop to read, like or comment on the friend's posts, that new data will be included in the data set and the News Feed will adjust accordingly.
Compiled By - Aparajita Mohanty
Manager - Business Development & Human Capital , Nirmalya Labs
|Comment 16-08-2017 10 : 51 AM|