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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:

  • The heavily hyped, self-driving Google car? The essence of machine learning.
  • Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life.
  • Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.
  • Fraud detection? One of the more obvious, important uses in our world today.

Let’s have a look on what are the popular Machine Learning methods:

  • Supervised learning algorithms are trained using labelled examples, such as an input where the desired output is known.
  • Unsupervised learning is used against data that has no historical labels. The system is not told the "right answer."
  • Reinforcement learning is often used for robotics, gaming and navigation. With reinforcement learning, the algorithm discovers through trial and error which actions yield the greatest rewards.

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?

  • Banking & Financial services: ML can be used to predict the customers who are likely to default from paying loans or credit card bills. This is of paramount importance as machine learning would help the banks to identify the customers who can be granted loans and credit cards.
  • Healthcare: It is used to diagnose deadly diseases (e.g. cancer) based on the symptoms of patients and tallying them with the past data of similar kind of patients.
  • Retail: It is used to identify products which sell more frequently (fast moving) and the slow moving products which help the retailers to decide what kind of products to introduce or remove from the shelf. Also, machine learning algorithms can be used to find which two / three or more products sell together. This is done to design customer loyalty initiatives which in turn help the retailers to develop and maintain loyal customers.

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 


    16-08-2017         10 : 51 AM

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