5 PROBLEMS THAT CAN BE EASILY SOLVED BY MACHINE LEARNING.

Machine Learning and Artificial Intelligence have gained prominence in the recent years with Google, Microsoft Azure and Amazon coming up with their Cloud Machine Learning platforms. But Interesting fact is that we have been experiencing machine learning without knowing it.

A simple usecase for our understanding :

The image tagging by Facebook and ‘Spam’ detection by email providers like gmail. e.g.  Facebook automatically tags uploaded images using face (image) recognition technique and Gmail recognizes the pattern or selected words to filter spam messages.

Let’s take a look at some of the important business problems solved by machine learning.

a. Detecting Spam :
Spam detection is one of the first use cases of ML. Long ago email service providers used pre-existing rule-based techniques to remove spam. But now the spam filters create new rules themselves using ML, this is achieved via ‘neural networks’ in its spam filters, Google’s Brain-like “neural networks” in its spam filters can learn to recognise junk mail and phishing messages by analysing rules across an enormous collection of computers. Today 75%+ phishing and spam emails don’t even land up in a customer inbox ! boom – what a relief ?

b. Sentiment Analysis & sales recommendation :

Given a set of tweets, Facebook posts of a given location or of a person during a specific time window and with the chain of events followed by it, ML can determine the sentiment and mood analysis of a Individual or a City or even that of a movie launch or even Election Results. Also Unsupervised learning enables a product based recommendation system. Given a purchase history for a customer and a large inventory of products, ML models can identify those products in which that customer will be interested and likely to purchase. The algorithm identifies hidden pattern among items and focuses on grouping similar products into clusters. A model of this decision process would allow a program to make recommendations to a customer and motivate product purchases. E-Commerce businesses such as Amazon has this capability. Unsupervised learning along with location detail is used by Facebook to recommend users to connect with others use

c. Predictive maintenance

Manufacturing industry can use artificial intelligence (AI) and ML to discover meaningful patterns in factory data. Boeing uses large amount of real-time flight data to understand, re-learn and make corrective and preventive maintenance of their planes. This helps them to understand the performance and utilisation in real-time. Corrective and preventive maintenance practices are costly and inefficient. Whereas predictive maintenance minimises the risk of unexpected failures and reduces the amount of unnecessary preventive maintenance activities.

d. Image recognition

Computer vision produces numerical or symbolic information from images and high-dimensional data. It involves machine learning, data mining, database knowledge discovery and pattern recognition. Potential business uses of image recognition technology are found in healthcare, automobiles – driverless cars, marketing campaigns, etc.

 

 

 

 

 

 

 

 

e. Speech recognition 

There is no single combination of sounds to specifically signal human speech, and individual pronunciations differ widely – machine learning can identify patterns of speech and help to convert speech to text. Alexa and Amazon Echo are great examples of Speech Recognition.

So all Machine Learning Problems go by the saying ” Begin with a priority problem, not a toy problem”. 

The reason why it has become complicated due to many companies who read about ML with enthusiasm and they decide to “find some way to use it.” This leads to teams without the real motivation or gusto (or committed resources) to drive an actual result. Pick a business problem that matters immensely, and seems to have a high likelihood of being solved.

So the definition of ML is – Its the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.